assignment on database and its types

Introduction to databases

What are databases, introduction, data persistence vs ephemeral storage, interacting with databases to manage your data, what responsibilities do databases have, alternatives to databases, what are databases used for, how do different roles work with databases, how do i work with databases as a developer.

Databases are essential components for many modern applications and tools. As a user, you might interact with dozens or hundreds of databases each day as you visit websites, use applications on your phone, or purchase items at the grocery store. As a developer, databases are the core component used to persist data beyond the lifetime of your application. But what exactly are databases and why are they so common?

In this article, we'll go over:

• what databases are
• how they are used by people and applications to keep track of various kinds of data
• what features databases offer
• what types of guarantees they make
• how they compare to other methods of data storage

Finally, we'll discuss how applications rely on databases for storing and retrieving data to enable complex functionality.

Databases are logical structures used to organize and store data for future processing, retrieval, or evaluation. In the context of computers, these structures are nearly always managed by an application called a database management system or DBMS . The DBMS manages dedicated files on the computer's disk and presents a logical interface for users and applications.

Database management systems are typically designed to organize data according to a specific pattern. These patterns, called database types or database models , are the logical and structural foundations that determine how individual pieces of data are stored and managed. There are many different database types, each with their own advantages and limitations. The relational model , which organizes data into cross-referenced tables, rows, and columns, is often considered to be the default paradigm.

DBMSs can make databases they govern accessible via a variety of means including command line clients, APIs, programming libraries, and administrative interfaces. Through these channels, data can be ingested into the system, organized as required, and returned as requested.

Databases store data either on disk or in-memory.

On disk storage is generally said to be persistent , meaning that the data is reliably saved for later, even if the database application or the computer itself restarts.

In contrast, in-memory storage is said to be ephemeral or volatile . Ephemeral storage does not survive application or system shutdown. The advantage of in-memory databases is that they are typically very fast.

In practice, many environments will use a mixture of both of these types of systems to gain the advantages of each type. For systems that accept new writes to the ephemeral layer, this can be accomplished by periodically saving ephemeral data to disk. Other systems use read-only in-memory copies of persistent data to speed up read access. These systems can reload the data from the backing storage at any time to refresh their data.

While the database system takes care of how to store the data on disk or in-memory, it also provides an interface for users or applications. The interfaces for the database must be able to represent the operations that external parties can perform and must be able to represent all of the data types that the system supports.

According to Wikipedia , databases typically allow the following four types of interactions:

• Data definition : Create, modify, and remove definitions of the data's structure. These operations change the properties that affect how the database will accept and store data. This is more important in some types of databases than others.
• Update : Insert, modify, and delete data within the database. These operations change the actual data that is being managed.
• Retrieval : Provide access to the stored data. Data can be retrieved as-is or can often be filtered or transformed to massage it into a more useful format. Many database systems understand rich querying languages to achieve this.
• Administration : Other tasks like user management, security, performance monitoring, etc. that are necessary but not directly related to the data itself.

Let's go over these in a bit more detail below.

Data definitions control the shape and structure of data within the system

Creating and controlling the structure that your data will take within the database is an important part of database management. This can help you control the shape, or structure, of your data before you ingest it into the system. It also allows you to set up constraints to make sure your data adheres to certain parameters.

In databases that operate on highly regular data, like relational databases, these definitions are often known as the database's schema . A database schema is a strict outline of how data must be formatted to be accepted by a particular database. This covers the specific fields that must be present in individual records as well as requirements for values such as data type, field length, minimum or maximum values, etc. A database schema is one of the most important tools a database owner has to influence and control the data that will be stored in the system.

Database management systems that value flexibility over regularity are often referred to as schema-less databases . While this seems to imply that the data stored within these databases has no structure, this is usually not the case. Instead, the database's structure is determined by the data itself and the application's knowledge of and relation to the data. The database usually still adheres to a structure, but the database management system is less involved in enforcing constraints. This is a design choice that has benefits and disadvantages depending on the situation.

Data updates to ingest, modify, and remove data from the system

Data updates include any operation that:

• Enters new data into the system
• Modifies existing entries
• Deletes entries from the database

These capabilities are essential for any database, and in many cases, constitute the majority of actions that the database system processes. These types of activities — operations that cause changes to the data in the system — are collectively known as write operations .

Write actions are important for any data source that will change over time. Even removing data, a destructive action, is considered a write operation since it modifies the data within the system.

Since write operations can change data, these actions are potentially dangerous. Most database administrators configure their systems to restrict write operations to certain application processes to minimize the chance of accidental or malicious data mangling. For example, data analytics, which use existing data to answer questions about a website's performance or visitors' behavior, require only read permission. On the other hand, the part of the application that records a user's orders needs to be able to write new data to the database.

Retrieving data to extract information or answer specific questions

Storing data is not very useful unless you have a way of retrieving it when you need it. Since returning data does not affect any of the information currently stored in the database, these actions are called read operations . Read operations are the primary way of gathering data already stored within a database.

Database management systems almost always have a straightforward way of accessing data by a unique identifier, often called a primary key . This allows access to any one entry by providing the key.

Many systems also have sophisticated methods of querying the database to return data sets that match specific criteria or return partial information about entries. This type of querying flexibility helps the database management system operate as a data processor in addition to its basic data storage capabilities. By developing specific queries, users can prompt the database system to return only the information they require. This feature is often used in conjunction with write operations to locate and modify a specific record by its properties.

Administering the database system to keep everything running smoothly

The final category of actions that databases often support is administrative functions. This is a broad, general class of actions that helps support the database environment without directly influencing the data itself. Some items that might fit into this group include:

• Managing users, permissions, authentication, and authorization
• Setting up and maintaining backups
• Configuring the backing medium for storage
• Managing replication and other scaling considerations
• Providing online and offline recovery options

This set of actions aligns with the basic administrative concerns common to any modern application.

Administrative operations might not be central to core data management functionality, but these capabilities often set similar database management systems apart. Being able to easily back up and restore data, implement user management that hooks into existing systems, or scale your database to meet demand are all essential features for operating in production. Databases that fail to pay attention to these areas often struggle to gain adoption in real world environments.

Given the above description, how can we generalize the primary responsibilities that databases have? The answer depends a lot on the type of database being used and your applications' requirements. Even so, there are a common set of responsibilities that all databases seek to provide.

Safeguarding data integrity through faithful recording and reconstituting

Data integrity is a fundamental requirement of a database system, regardless of its purpose or design. Data loaded into the database should be able to be retrieved dependably without unexpected modification, manipulation, or erasure. This requires reliable methods of loading and retrieving data, as well as serializing and deserializing the data as necessary to store it on physical media.

Databases often rely on features to verify data as it is written or retrieved, like checksumming , or to protect against issues caused by unexpected shutdowns, using techniques like write-ahead logs , for example. Data integrity becomes more challenging the more distributed the data store is, as each part of the system must reflect the current desired state of each data item. This is often achieved with more robust requirements and responses from multiple members whenever data is changed in the system.

Offering performance that meets the requirements of the deployment environment

Databases must perform adequately to be useful. The performance characteristics you need depend heavily on the particular demands of your applications. Every environment has unique balance of read and write requests and you will have to decide on what acceptable performance means for both of those categories.

Databases are generally better at performing certain types of operations than others. Operational performance characteristics are often a reflection of the type of database used, the data schema or structure, and the operation itself. In some cases, features like indexing , which creates an alternative performance-optimized store of commonly accessed data, can provide faster retrieval for these items. Other times, the database may just not be a good fit for the access patterns being requested. This is something to consider when deciding on what type of database you need.

Setting up processes to allow for safe concurrent access

While this isn't a strict requirement, practically speaking, databases must allow for concurrent access. This means that multiple parties must be able to work with the database at the same time. Records should be readable by any number of users at the same time and writable when not currently locked by another user.

Concurrent access usually means that the database must implement some other fundamental features like user accounts, a permissions system, and authentication and authorization mechanisms. It must also develop strategies for preventing multiple users from attempting to manipulate the same data concurrently. Record locking and transactions are often implemented to address these concerns.

Retrieving data individually or in aggregate

One of the fundamental responsibilities of a database is the ability to retrieve data upon request. The requests might be for individual pieces of data associated with a single record, or they may involve retrieving the data found in many different records. Both of these cases must be possible in most systems.

In most databases, some level of data processing is provided by the database itself during retrieval. These can include the following types of operations:

• Searching by criteria
• Filtering and adhering to constraints
• Extracting specific fields
• Averaging, sorting, etc.

These options help you articulate the data you'd like and the format that would be most useful.

Before we move on, we should briefly take a look at what your options are if you don't use a database.

Most methods that store data can be classified as a database of some kind. A few exception include the following.

Local memory or temporary filesystems

Sometimes applications produce data that is not useful or that is only relevant for the lifetime of the application. In these cases, you may wish to keep that data in memory or offload it to a temporary filesystem since you won't need it once the application exits. For cases where the data is never useful, you may wish to disable output entirely or log it to /dev/null .

Serializing application data directly to the local filesystem

Another instance where a database might not be required is where a small amount of data can be serialized and deserialized directly instead. This is only practical for small amounts of data with a predictable usage pattern that does not involve much, if any, concurrency. This does not scale well but can be useful for certain cases, like outputting local log information.

Storing file-like objects directly to disk or object-storage

Sometimes, data from applications can be written directly to disk or an alternative store instead of storing into a database. For instance, if the data is already organized into a file-oriented format, like an image or audio file, and doesn't require additional metadata, it might be easiest to store it directly to disk or to a dedicated object store.

Almost all applications and websites that are not entirely static rely on a database somewhere in their environment. The primary purpose of the database often dictates the type of database used, the data stored, and the access patterns employed. Often multiple database systems are deployed to handle different types of data with different requirements. Some databases are flexible enough to fulfill multiple roles depending on the nature of different data sets.

Let's take a look at an example to discuss the touchpoints a typical web application may have with databases. We'll pretend that the application contains a basic storefront and sells items it tracks in an inventory.

Storing and processing site data

One of the primary uses for databases is storing and processing data related to the site. These items affect how information on the site is organized and, for many cases, constitute most of the "content" of the site.

In the example application mentioned above, the database would populate most of the content for the site including product information, inventory details, and user profile information. This means that the database or some intermediary cache would be consulted each time a product list, a product detail page, or a user profile needs to be displayed.

A database would also be involved when displaying current and past orders, calculating shipping cost, and applying discounts by checking discount codes or calculating frequent customer rewards. Our example site would use the database system to correctly build orders by combining product information, inventory, and user information. The composite information that is recorded in an order would be stored in a database again to track order processing, allow returns, cancel or modify orders, or enable better customer support.

Analyzing information to help make better decisions

The actions in the last category were related to the basic functionality of the website. While these are very important for handling the data requirements of the application layer, they don't represent the entire picture.

Once your web application begins registering users and processing orders, you probably want to be able to answer detailed questions about how different products are selling, who your most profitable users are, and what factors influence your sales. These are analytical questions that can be run at any time to gather up-to-date intelligence about your organization's trends and performance.

These types of operations are often called business intelligence or analytics . Together, they help organizations understand what happened in the past and to make informed changes. Database systems store most of the data used during these processes and must provide the appropriate tooling or querying capabilities to answer questions about it.

In our example application, the databases could be queried to answer questions about product trends, user registration numbers, which states we ship to the most, or who our most loyal users are. These relatively basic queries can be used to compose more complex questions to better understand and control factors that influence product performance.

Managing software configuration

Some types of databases are used as repositories for configuration values for other software on the network. These serve as a central source of truth for configuration values on the network. As new services are started up, they are configured to check the values for specific keys at the configuration database's network address. This enables you to store all of the information needed to bootstrap services in one location.

After bootstrapping, applications can be configured to watch the keys related to their configuration for changes. If a change is detected, the application can reconfigure itself to use the new configuration. This process is sometimes orchestrated by a management process that rolls out the new values over time by spinning old services down as the new services come up, changing over the active configuration over time to maintain availability.

Our application could use this type of database to store persistent configuration data for our entire application environment. Our application servers, web servers, load balancers, messaging queues, and more could be configured to reference a configuration database to get their production settings. The application's developers could then modify the behavior of the environment by tweaking configuration values in a central location.

Collecting logs, events, and other output

Running applications that are actively serving requests can generate a lot of output. This includes log files, events, and other output. These can be written to disk or some other unmanaged location, but this limits their usefulness. Collecting this type of data in a database makes it easier to work with, spot patterns, and analyze events when something unexpected happens or when you need to find out more about historical performance.

Our example application might collect logs from each of our systems in one database for easier analysis. This can help us find correlations between events if we're try to analyze the source of problems or understand the health of our environment as a whole.

Separately, we might collect metrics produced by our infrastructure and code in a time series database , a database specifically designed to track values over time. This database could be used to power real time monitoring and visualization tools to provide the application's development and operations teams with information about performance, error rates, etc.

Databases are fundamental to the work of many different roles within organizations. In smaller teams, one or a few individuals may be responsible for carrying out the duties of various roles. In larger companies, these responsibilities are often segmented into discrete roles performed by dedicated individuals or teams.

Data architects

Data architects are responsible for the overall macro structure of the database systems, the interfaces they expose to applications and development teams, and the underlying technologies and infrastructure required to meet the organization's data needs.

People in this role generally decide on appropriate database model and implementation that will be used for different applications. They are responsible for implementing database decisions by investigating options, deciding on technology, integrating it with existing systems, and developing a comprehensive data strategy for the organization. They deal with the data systems holistically and have a hand in deciding on and implementing data models for various projects.

DBAs (database administrators)

Database administrators , or DBAs, are individuals who are responsible for keeping data systems running smoothly. They are responsible for planning new data systems, installing and configuring software, setting up database systems for other parties, and managing performance. They are also often responsible for securing the database, monitoring it for problems, and making adjustments to the system to optimize for usage patterns.

Database administrators are experts on both individual database systems as well as how to integrate them well with the underlying operating system and hardware to maximize performance. They work extensively with teams that use the databases to help manage capacity and performance and to help teams troubleshoot issues with the database system.

Application developers

Application developers interact with databases in many different ways. They develop many of the applications that interact with the database. This is very important because these are almost always the only applications that control how individual users or customers interact with the data managed by the database system. Performance, correctness, and reliability are incredibly important to application developers.

Developers manage the data structures associated with their applications to persist their data to disk. They must create or use mechanisms that can map their programming data to the database system so that the components can work together in harmony. As applications change, they must keep the data and data structures within the database system in sync. We'll talk more about how developers work with databases later in the article.

SREs (site reliability engineers) and operations professionals

SREs (site reliability engineers) and operations professionals interact with database systems from an infrastructure and application configuration perspective. They may be responsible for provisioning additional capacity, standing up database systems, ensuring database configuration matches organizational guidelines, monitoring uptime, and managing back ups.

In many ways, these individuals have overlapping responsibilities with DBAs, but are not focused solely on databases. Operations staff ensure that the systems that applications that the rest of the organization rely on, including database systems, are functioning reliably and have minimal downtime.

Business intelligence and data analysts

Business intelligence departments and data analysts are primarily interested in the data that is already collected and available within the database system. They work to develop insights based on trends and patterns within the data so that they can predict future performance, advise the organization on potential changes, and answer questions about the data for other departments like marketing and sales.

Data analysts can generally work exclusively with read-only access to data systems. The queries they run often have dramatically different performance characteristics than those used by the primary applications. Because of this, they often work with database replicas, or copies, so that they can perform long-running and performance intensive aggregate queries that might otherwise impact the resource usage of the primary database system.

So how do you actually go about working with databases as an application developer? On a basic level, if your application has to manage and persist state, working with a database will be an important part of your code.

Translating data between your application and the database

You will need to create or use an existing interface for communicating with the database. You can connect directly to the database using regular networking functions, leverage simple libraries, or higher-level programming libraries (e.g. query builders or ORMs).

ORMs , or object-relational mappers, are mapping layers that translate the tables found in relational database to the classes used within object-oriented program languages and vice versa. While this translation is often useful, it is never perfect. Object-relational impedance mismatch is a term used to describe the friction caused by the difference in how relational databases and object-oriented programs structure data.

Although relational databases and object-oriented programming describe two specific design choices, the problem of translating between the application and database layer is a generalized one that exists regardless of database type or programming paradigm. Database abstraction layer is a more general term for software with the responsibility of translating between these two contexts.

Keeping structural changes in sync with the database

One important fact you'll discover as you develop your applications is that since the database exists outside of your codebase, it needs special attention to cope with changes to your data structure. This issue is more prevalent in some database designs than others.

The most common approach to synchronizing your application's data structures with your database is a process called database migration or schema migration (both known colloquially simply as migration). Migration involves updating your database's structure to reflect changes as your application's data model evolves. These usually take the form of a series of files, one for each evolution, that contain the statements needed to transform the database into the new format.

Protecting access to your data and sanitizing input

One important responsibility when working with databases as a developer is ensuring that your applications don't allow unauthorized access to data. Data security is a broad, multi-layered problem with many stakeholders. Ultimately, some of the security considerations will be your duty to look after.

Your application will require privileged access to your database to perform routine tasks. For safety, the database's authorization framework can help restrict the type of operations your application can perform. However, you need to ensure that your application restricts those operations appropriately. For example, if your application manages user profile data, you have to prevent a user from manipulating that access to view or edit other users' information.

One specific challenge is sanitizing user input. Sanitizing input means taking special precautions when operating on any data provided by a user. There is a long history of malicious actors using normal user input mechanisms to trick applications into revealing sensitive data. Crafting your applications to protect against these scenarios is an important skill.

Databases are an indispensable component in modern application development. Storing and controlling the stateful information related to your application and its environment is an important responsibility that requires reliability, performance, and flexibility.

Fortunately, there are many different database options designed to fulfil the requirements of different types of applications. In our next article , we'll take an in-depth look at the different types of databases available and how they can be used to match different types of application requirements.

Prisma is one way to make it easy to work with databases from your application. You can learn more about what Prisma offers in our Why Prisma? page .

Prisma database connectors allow you to connect Prisma to many different types of databases. Check out our docs to learn more.

Databases store data either on disk or in-memory. On disk storage is generally said to be persistent , meaning that the data is reliably saved for later, even if the database application or the computer itself restarts.

Database administrators , or DBAs, are individuals who are responsible for keeping data systems running smoothly. They are responsible for planning new systems, installing and configuring software, setting up database systems for other parties, and managing performance.

A database abstraction layer is an application programming interface which unifies the communication between a computer application and a database.

Database management refers to the actions taken to work with and control data to meet necessary conditions throughout the data lifecycle.

Some database management tasks include performance monitoring and tuning, storage and capacity planning, backup and recovery data, data archiving, data partitioning, replication, and more.

Database management systems (DBMS) are software systems used to store, retrieve, and run queries on data. They serve as an interface between end-users and a database to perform CRUD operations.

Justin Ellingwood

Prisma's data guide.

A growing library of articles focused on making databases more approachable.

What Is a Database?

A database is simply a structured and systematic way of storing information to be accessed, analyzed, transformed, updated and moved (to other databases). 

To begin understanding databases, consider an Excel notebook or Google sheet. Spreadsheets like these are a basic form of a table. Databases are almost exclusively organized in tables and those tables have rows and columns. So, think of a simple database as a collection of spreadsheets (or tables) joined together in a systematic way.

Database Definition

A database is a way for organizing information, so users can quickly navigate data, spot trends and perform other actions. Although databases may come in different formats, most are stored on computers for greater convenience.

Databases are stored on servers either on-premises at an organization’s office or off-premises at an organization’s data center (or even within their cloud infrastructure). Databases come in many formats in order to do different things with various types of data. 

Related Reading From Built In Experts Python Databases 101: How to Choose a Database Library

Why Do We Use Databases?

Computerized databases were first introduced to the world in the 1960s and have since become the foundation for products, analysis, business processes and more. Many of the services you use online every day (banking, social media, shopping, email) are all built on top of databases.

Today, databases are used for many reasons.

Databases Hold Data Efficiently

We use databases because they are an extremely efficient way of holding vast amounts of data and information. Databases around the world store everything from your credit card transactions to every click you make within one of your social media accounts. Given there are nearly eight billion people on the planet,  that’s a lot of data . 

Databases Allow Smooth Transactions

Databases allow access to various services which, in turn, allow you to access your accounts and perform transactions all across the internet. For example, your bank’s login page will ping a database to figure out if you’ve entered the right password and username. Your favorite online shop pings your credit card’s database to pull down the funds needed for you to buy that item you’ve been eyeing. 

Databases Update Information Quickly

Databases allow for easy information updates on a regular basis. Adding a video to your TikTok account, directly depositing your salary into your bank account or buying a plane ticket for your next vacation are all updates made to a database and displayed back to you almost instantaneously. 

Databases Simplify Data Analysis

Databases make research and data analysis much easier because they are highly structured storage areas of data and information. This means businesses and organizations can easily analyze databases once they know how a database is structured. Common structures (e.g. table formats, cell structures like date or currency fields) and common database querying languages (e.g.,  SQL ) make database analysis easy and efficient. 

What Is a Database Management System?

A database management system (DBMS) is a software package we use to create and manage databases. In other words, a DBMS makes it possible for users to actually interact with the database. In other words, the DBMS is the user interface (UI) that allows us to access, add, modify and delete content from the database. There are several types of database management systems, including relations, non-relational and hierarchical.

Evolution of Databases

Storing information is nothing new, but the rise of computers in the 1960s marked a shift toward more digital forms of databases. While working for GE, Charles Bachman created the Integrated Data Store, ushering in a new age of computerized databases. IBM soon followed suit with its Information Management System, a hierarchical database. 

In the 1970s, IBM’s Edgar F. Codd released a paper touting the benefits of relational databases, leading to IBM and the University of California, Berkeley releasing their own models. Relational databases became popular in the following years, with more businesses developing models and using Structured Query Language (SQL). Even though object-oriented databases became an alternative in the 1980s, relational databases remained the gold standard. 

The invention of the World Wide Web led to greater demand for databases in the 1990s. MySQL and NoSQL databases entered the scene, competing with the commercial databases developed by businesses. Object-oriented databases also began to replace relational databases in popularity.        

During the 2000s and 2010s, organizations began to collect larger volumes of data, and many turned to the scalability offered by NoSQL databases. Distributed databases provided another way to organize this proliferating data , storing it away in multiple locations.  

Types of Databases

There are many types of databases used today. Below are some of the more prominent ones.

1. Hierarchical Databases 

Hierarchical databases were the earliest form of databases. You can think of these databases like a simplified family tree. There’s a singular parent object (like a table) that has child objects (or tables) under it. A parent can have one or many child objects but a child object only has one parent. The benefit of these databases are that they’re incredibly fast and efficient plus there’s a clear, threaded relationship from one object to another. The downside to hierarchical databases is that they’re very rigid and highly structured. 

2. Relational Databases  

Relational databases are perhaps the most popular type of database. Relational databases are set up to connect their objects (like tables) to each other with keys. For example, there might be one table with user information (name, username, date of birth, customer number) and another table with purchase information (customer number, item purchased, price paid). In this example, the key that creates a relationship between the tables is the customer number. 

3. Non-Relational or NoSQL Databases  

Non-relational databases were invented more recently than relational databases and hierarchical databases in response to the growing complexity of web applications. Non-relational databases are any database that doesn’t use a relational model. You might also see them referred to as  NoSQL databases . Non-relational databases store data in different ways such as unstructured data, structured document format or as a graph. Relational databases are based on a rigid structure whereas non-relational databases are more flexible.

4. Cloud Databases

Cloud databases refer to information that’s accessible in a hybrid or cloud environment. All users need is an internet connection to reach their files and manipulate them like any other database. A convenience of cloud databases is that they don’t require extra hardware to create more storage space. Users can either build a cloud database themselves or pay for a service to get started.

5. Centralized Databases

Centralized databases are contained within a single computer or another physical system. Although users may access data through devices connected within a network, the database itself operates from one location. This approach may work best for larger companies or organizations that want to prioritize data security and efficiency.

6. Distributed Databases

Distributed databases run on more than one device. That can be as simple as operating several computers on the same site, or a network that connects to many devices. An advantage of this method is that if one computer goes down, the other computers and devices keep functioning.  

7. Object-Oriented Databases 

Object-oriented databases perceive data as objects and classes. Objects are specific data — like names and videos — while classes are groups of objects. Storing data as objects means users don’t have to distribute data across tables. This makes it easier to determine the relationships between variables and analyze the data. 

8. Graph Databases

Graph databases highlight the relationships between various data points. While users may have to do extra work to determine trends in other types of databases, graph databases store relationships right next to the data itself. Users can then immediately see how various data points are connected to each other.  

What Are the Components of a Database?

The components of a database vary slightly depending on whether the database is hierarchical, relational or non-relational. However, here’s a list of database components you might expect to be associated with any database.

The database schema is essentially the  design of the database . A schema is developed at the early conceptual stages of building a database. It’s also a valuable source of ongoing information for those wanting to understand the database’s design. 

Constraints and Rules

Databases use constraints to determine what types of tables can (and cannot) be stored and what types of data can live in the columns or rows of the database tables, for example. These constraints are important because they ensure data is structured, less corruptible by unsanctioned  data structures and that the database is regulated so users know what to expect. These constraints are also the reason why databases are considered rigid.

Metadata is essentially the data about the data. Each database or object has metadata, which the database software reads in order to understand what’s in the database. You can think of metadata as the database schema design and constraints combined together so a machine knows what kind of database it is and what actions can (or can’t) be performed within the database. 

Query Language

Each database can be queried. In this case, “queried” means people or services can access the database. That querying is done by way of a particular language or code snippet. The most common querying language is SQL (Structured Query Language) but there are also many other languages and even SQL variations like  MySQL , Presto and Hive.

Each database is a collection of objects. There are a few different types of objects stored within databases such as tables, views, indexes, sequences and synonyms. The most well known of these are tables, like spreadsheets, that store data in rows and columns. You may also hear the term “object instance,” which is simply an instance or element of an object. For example, a table called “Transactions” in a database is an instance of the object-type table.

Database Advantages

The structured nature of databases offers a range of benefits for professional and casual users alike. Below are some of the more prominent advantages:  

• Improved data sharing and handling
• Improved data storage capacity
• Improved data integrity and data security
• Reduced data inconsistency 
• Quick data access
• Increased productivity
• Improved data-driven decision making  

Database Disadvantages

Although databases can be helpful for many, there are some limitations to consider before investing in a database: 

• High complexity
• Required dedicated database management staff
• Risk of database failure​

Applications of Databases

When used correctly, databases can be a helpful tool for organizations in various industries looking to better arrange their information. Common use cases include:

• Healthcare: storing massive amounts of patient data .
• Logistics: monitoring and analyzing route information and delivery statuses.
• Insurance: storing customer data like addresses, policy details and driver history.
• Finance: handling account details, invoices, stock information and other assets.
• E-commerce: compiling and arranging data on products and customer behavior.
• Transportation: storing passengers’ names, scheduled flights and check-in status.
• Manufacturing: keeping track of machinery status and production goals.
• Marketing: collecting data on demographics, purchasing habits and website visits.
• Education: tracking student grades, course schedules and more.
• Human resources: organizing personnel info, benefits and tax information.

Future of Databases

As organizations handle increasing amounts of data, future databases must be able to keep up. Users will expect databases to be accessible across the globe and able to deal with limitless volumes of data. As a result, it’s likely that more companies will migrate their data to cloud environments. The percent of data stored in the cloud doubled between 2015 and 2022, and there’s reason to believe this percentage will only grow in the years to come. 

With the increase in data has also come a spike in cybersecurity threats , so organizations can be expected to complement their cloud environments with reinforced security measures . Databases will become more easily accessible only for authorized personnel while companies adopt tools and best practices for keeping their data out of the wrong hands.

Frequently Asked Questions

What is the difference between a database and a spreadsheet.

Spreadsheets organize data into rows and columns, with each individual cell housing the actual data. Databases also employ rows and columns, but each cell contains a record of data gathered from an external table. As a result, databases provide more ways to arrange and structure information as opposed to spreadsheets.

What is the most commonly used database type?

The most commonly used database type is the relational database.

What is the definition of a database?

A database is highly organized information that is designed to be easily accessible and navigable for users. Most databases are stored on computers, making it possible to quickly analyze, transform and manipulate data in other ways.

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Database Management Systems and SQL – Tutorial for Beginners

Database Management Systems and SQL are two of the most important and widely used tools on the internet today.

You use a Database Management System (DBMS) to store the data you collect from various sources, and SQL to manipulate and access the particular data you want in an efficient way.

Many different businesses use these tools to increase their sales and improve their products. Other institutions like schools and hospitals also use them to improve their administrative services.

In this article, you will learn about:

• The basics of DBMS and SQL
• The most important features of DBMS and SQL
• The reasons you should learn DBMS and SQL.

What Does a DBMS Do?

DBMS stands for Database Management System, as we mentioned above. SQL stands for Structured Query Language.

If you have lots of data that you need to store, you don't just want to keep it anywhere – then there would be no sense of what that huge amount of data means or can tell you. That's why we use a DBMS.

A database is basically where we store data that are related to one-another – that is, inter-related data. This inter-related data is easy to work with.

A DBMS is software that manages the database. Some of the commonly used DBMS (software) are MS ACCESS, MySQL, Oracle, and others.

Suppose you have some data like different names, grades, and ID numbers of students. You'd probably prefer to have that data in a nice table where a particular row consists of students’ names, grades, and ID numbers. And to help you organize and read that data efficiently, you'll want to use a DBMS.

Using a DBMS goes hand in hand with SQL. This is because when you store data and want to access and alter it, you'll use SQL.

A database stores data in various forms like schemas, views, tables, reports, and more.

Types of DBMS

There are two types of DBMS.

First, you have Relational Databases (RDBMS). In these types of databases, data is stored in the format of tables by the software. In an RDBMS, each row consists of data from a particular entity only.

Some of the RDBMS commonly used are MySQL, MSSQL, Oracle, and others.

Then you have Non-Relational Databases. In these databases, data is stored in the form of key and value pairs.

Some of the Non-Relational DBMSs commonly used are MongoDB, Amazon, Redis, and others.

Components of a DBMS

There are mainly four components of a DBMS which you can understand by checking out the image below:

You have your Users. There can be multiple users, like someone who manages the database (the database administrator), system developers, and also those who are just regular users like the customer.

You also have the Database Application. The application of a database can be either departmental or personal or may be for internal use in an organization.

Then you have the DBMS, which we've been discussing. This is software that helps the users create the database and access the data inside it in an efficient manner.

Finally, you have the Database, which is a collection of data stored in the form of a single unit.

One important feature of a DBMS is that it helps reduce the redundancy in the data stored. Having the same data stored at multiple locations in a database is called redundancy.

To eliminate and reduce the redundancy in the database, normalization is used.

Normalization is the process of structuring the data in an RDBMS by removing anomalies. It is important to enable easy retrieval of data from the database as well as to add or delete data without losing consistency. This might be implemented with the help of “Normal Forms” in DBMS. These normal forms help in establishing relations in a relational database instead of having to redefine existing fields again and again. In this way, normalization reduces redundancy.

What is SQL?

SQL is a database language. SQL is used widely and almost all Relational Database Management Systems can recognize it.

SQL contains a set of commands that enable you to create a database. You can also use it to execute commands in your Relational Database Management System.

SQL has certain advantages which have helped it thrive from the 1970s until now. It is widely accepted by both people and platforms, in part because of the following features:

• SQL is fast
• SQL is a very high-level language
• SQL is a platform-independent language
• SQL is a standardized language
• SQL is a portable language

Along with all the features mentioned above, you need almost no coding skills to work with SQL.

SQL performs a variety of tasks like creating, altering, maintaining and retrieving data, setting properties, and so on. All the tasks are done based on the commands you write, and these commands are grouped into various categories like DDL commands, DML commands, DCL commands, and so on.

Let's discuss some of the frequently used commands and their types.

DDL commands

DDL stands for Data Definition Language. It includes the set of commands that you use to perform various tasks related to data definition. You use these commands to specify the structure of the storage and methods through which you can access the database system.

You use DDL commands to perform the following functions:

• To create, drop, and alter.
• To grant and revoke various roles and privileges.
• Maintenance commands

Example DDL commands include CREATE , ALTER , DROP , and TRUNCATE .

DML commands

DML stands for Data Manipulation Language. As the name suggests, it consists of commands which you use to manipulate the data.

You use these commands for the following actions:

• Modification

Example DML commands are SELECT , INSERT , UPDATE , and DELETE .

TCL commands

TCL stands for Transaction Control Language. As the name says, you use these commands to control and manage transactions.

One complete unit of work that involves various steps is called a transaction.

You use these commands for the following purposes:

• To create savepoints
• To set properties of the transaction going on
• To undo the changes to the database (permanent)
• To make changes in the database (permanent)

Example TCL commands include COMMIT , ROLLBACK , and SAVE TRANSACTION .

How to Write Basic Queries in SQL

There are various keywords you use in SQL like SELECT, FROM, WHERE, and others. These SQL keywords are not case-sensitive.

To create a table called Student that has a name, roll numbers, and marks in it, you can write:

Here CREATE, TABLE, and NOT NULL are keywords. You use CREATE and TABLE to create a table and NOT NULL to specify that the column cannot be left blank while making a record.

To make a query from a table, you'll write:  

You use the ‘select’ keyword to pull the information from a table. The ‘From’ keyword selects the table from which the information is to be pulled. The ‘where’ keyword specifies the condition to be specified.

For example, say we want to retrieve the marks from the student table that has data for marks, roll numbers, and names. The command would be as follows:

If you want to learn more about SQL for beginners, you can check out this cheatsheet that'll teach you the basics pretty quickly.

You can also go through this Relational Database Course for Beginners to get a more solid understanding of the query language.

Why Are DBMS and SQL Important?

Being able to work with DBMS and SQL are some of the most critical skills in today’s world. After all, you know what they say - "Data is the new oil." So you should know how to work with it effectively.

Here are a few reasons why you should learn how to use at least one DBMS and SQL.

Reasons to Learn How to Use a DBMS

If you're storing an extremely large amount of data.

If your organization needs to store a huge amount of data, you'll want to use a DBMS to keep them organized and be able to access them easily.

DBMS store the data in a very logical manner making it very easy to work with a humongous amount of Data. You can read more about database management systems in this tutorial by freeCodeCamp , in this Wiki , and on Scaler for a better understanding of data storage in DBMS.

If you're doing data mining

Data mining is the process of extracting usable data that includes only relevant information from a very large dataset. Using a DBMS, you can perform data mining very efficiently. For managing the data, you use CRUD operations which stands for Create, Read, Update, and Delete. You can perform these operations with a DBMS easily and efficiently.

Integrity constraint and scalability

The data you store in your database satisfies integrity constraints. Integrity constraints are the set of rules that are already defined and which are responsible for maintaining the quality and consistency of data in that database. The DBMS makes sure that the data is consistent. Scalability is another important feature of a DBMS. You can insert a lot of data into a database very easily and it will be accessible to the user quickly and with some basic queries. You do not need to write new code and spend lots of time and money on expanding the same database.

When you have multiple user interfaces

When you're using a DBMS, you can have multiple users access the system at the same time. Just like in a UNIX operating system two users can log into a single account at the same time.

DBMS makes storing data simple. You can also add security permissions on data access to make sure access is restricted and the privacy of the data remains intact. DBMS protects the confidentiality, availability, integrity, and consistency of the data stored in it. Along with making the data secure it reduces the time taken to develop an application and makes the process efficient.

Learning a DBMS is an in-demand skill:

Most companies out there – big or small – have lots of data to work with. And so they'll need people to analyze it.

If you know how to use a DBMS, you can use those skills in almost all data-oriented technologies. So once you learn DBMS, it will be easy to work on any data-driven technology.

Reasons to Learn SQL

Since SQL is a language that is used for database management, some of the above points also apply to learning it (such as data storage, data mining, and so on).

Here are some of the additional reasons you should learn SQL.

SQL is relatively easy to learn

SQL is quite easy to learn in the context of database management. SQL queries resemble the simple English we use in our day-to-day life. For example, if we want to make a table named Topics, we just have to use the command:

Understanding how a computer works helps you learn other skills related to computers like any programming language, spreadsheet software like MS Excel, and word processing software like MS word.

You also use SQL to manage data on various platforms like SQLite .

SQL is standardized

SQL was developed in the 1970s and has been extensively used for more than 50 years without many significant changes made to it. This makes it a standard skill for working with data, so typically when you apply for a job, they will be using SQL for data storage and management purposes. This general standardization also makes it easier to learn because you don't need to constantly update your knowledge, again and again, to be adept at it.

SQL is easy to troubleshoot

Any error you get while using SQL will show a clear message about what's going on in very simple English.

For example, if you are trying to use a table or any database that does not exist, it will show the error that the table or the database you are trying to access does not exist.

There is the concept of exception handling in SQL also just like any other           programming language.

Exception handling is used for handling query runtime errors with the TRY CATCH construct. The TRY block is used to specify the set of statements that need to be checked for an error, while CATCH block executes certain statements in case an error has occurred. Exception handling is crucial for writing bug-free code.

Easy to manipulate data

Data manipulation refers to Adding (or inserting), deleting (removing), and modifying (updating) the data in a database. The data you store in the SQL is dynamic in nature which makes it easy for you to manipulate the data at any point in time.

You can also retrieve data easily using a single-line SQL command.And if you want to present the data in the form of charts or graphs, then SQL plays a key role in that and makes data visualization easy for you.

Client and server data sharing

Whenever an application is used, the data stored in the database management system is retrieved based on the option selected by the user. To create and manage the servers, SQL is used. SQL is used to navigate through the large amount of data stored in the database management system.

Easy to sync data from multiple sources

You'll come across many cases when you have to get data from multiple sources and combine them to get the desired output. This means you'll be dealing with outputs from multiple sources at one time, which can be time-consuming and a tedious job.

But when you use SQL, it is much easier to handle data from multiple sources at the same time and combine them to get the desired output.

In SQL you can use the UNION operation to combine data, like this:

Using this combines the columns “name” and “order_id” from the “customers” and “orders” tables, respectively, and renders the combined table.

Flexibility, versatility, and data analysis

SQL is a programming language, but the scope of this language is not only limited to programming tasks. You can use it for various purposes like in the finance sector and in sales and marketing, as well. By executing a few queries you can get the data you need and analyze it for your purposes.

There are various roles that are specific to SQL like SQL developer, SQL database Administrator, Database Tester, SQL Data analyst DBA, Data Modeler, and more. You can learn more about salary insights here .

Another important role is that of a data analyst. The process of cleansing, modeling, and transforming data to draw conclusions from it based on certain information is called Data analysis.

The role of a data analyst is important in any organization as it helps in analyzing trends and making fast and flexible decisions on the basis of the available data.

SQL and DBMS are two of the most in-demand skills for Data Analysis.

How DBMS and SQL Work Together

DBMS and SQL are interdependent and cooperate to make the data organized and accessible. Now, let's understand how SQL works in synchronization with a Database Management System.

SQL is the way you interact with the database management system. You use it to retrieve, insert, update, or delete data (CRUD operations), among other things.

When you execute a SQL command, the DBMS figures out the most efficient way to execute that command. The interpretation of the task to be performed is determined by the SQL engine.

The classic query engine is used to handle all the non-SQL queries, but it will not handle any logical files.

The query processor interprets the queries of the user and translates them into a database-understandable format.

The parser is used for translation purposes (in query processing). It also checks the syntax of the query and looks for errors, if present.

The optimisation engine, as the name suggests, optimises the performance of the database with the help of useful insights.

The DBMS engine is the underlying software component for performing CRUD operations on the database.

The file manager is used for managing the files in the database, one at a time.

And the transaction manager is used for managing the transactions to maintain concurrency while accessing data.

In this article, we have discussed the basics of DBMS and SQL and why you should learn these skills.

We have discussed the purpose and importance of DBMS and SQL, what they're used for, and what professionals who work with databases and SQL do.

After reading this article you have a good understanding of where knowledge of DBMS and SQL can take you. Happy Learning!

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Exploring Different Types of Databases: A Guide for Data Engineers

Databases come in various configurations, each designed to support different use cases, data types, and data models.

For example, relational databases are built to record transactions and support analytical queries, and NoSQL databases are designed for real-time data processing.

To help you understand the different types of database systems, this article will explain each type of database and its key features. We’ve also listed the four key considerations for choosing the right solution for your project.

Types of Databases Comparison

Here is a quick comparison of the six main types of databases:

Relational Databases (RDBMS)

Relational databases are used to store structured data . They organize data into tables with columns and rows. Each row represents a unique instance of data, and each column represents a different attribute or property of that data.

A relational database is a collection of tables. Primary and foreign keys establish relationships between tables.

Data analysts use SQL (structured query language) and relational database management system (RDBMS) software to query and manipulate data. 

Relational database solutions normalize data and implement constraints to maintain data integrity and consistency.

RDBMS tools can be used to create operational databases for real-time OLTP (Online Transactional Processing) workloads that record simple database transactions in real-time.

Data from relational databases can also be used for data warehousing to support data integration .

Key features

Relational database management systems have three key characteristics:

• ACID properties: Relational databases comply with the ACID properties (Atomicity, Consistency, Isolation, Durability), which ensure that database transactions are processed reliably and consistently. ‍
• Schema-based data organization: This database type uses a fixed, predefined schema to store data in tables. ‍
• SQL as a query language: Structured Query Language (SQL) is a standardized programming language used to retrieve, insert, update, and delete data from tables in a relational database management system.

Popular relational databases

1. MySQL: MySQL is an open-source, feature-rich RDBMS that supports database transactions, ACID compliance, foreign keys, triggers, and stored procedures. It also has several tools for database management.

2. PostgreSQL: PostgreSQL is an open-source RDBMS that drives large-scale enterprise applications where customization and extensibility are important. It has dynamic features for centralized database management and is supported by a large, active community of users and developers.

3. Microsoft SQL Server: Microsoft SQL Server is an enterprise database for managing and storing large amounts of data. It includes many advanced features for data warehousing and in-memory OLTP for real-time analytics.

4. Oracle Database: Oracle DB is a high-performance, scalable RDBMS commonly used by large-scale enterprises. It is used in mission-critical applications that require high performance, scalability, and reliability.

NoSQL Databases

NoSQL (Not only SQL) databases are non-relational databases. Instead of a fixed data model, they use varying data models and can handle semi-structured and unstructured data.

A NoSQL database has a flexible schema, which makes it more adaptable to changing data structures. It provides the flexibility needed for the ever-evolving use cases of modern data teams.

These types of databases are highly scalable and can handle big data workloads easily. They drive applications that require high availability and real-time processing, such as social media, gaming, and e-commerce.

Key types of NoSQL databases

There are four main types of NoSQL databases:

Document databases

Document-oriented databases, also called document stores, store data as documents. Every document is a self-contained entity. It can have any number of key-value pairs, where the value can be a scalar, an array, or another nested document.

Document databases are schema-less, meaning the document’s structure can vary from one document to another.

This flexibility makes a document database ideal for handling evolving data structures and storing business data that is not well-defined in advance, like user-generated content, log files, and sensor data. Some document stores also have advanced querying features. 

Examples of document databases include MongoDB, Couchbase, and RavenDB.

Key-value databases

This type of database stores data as key-value pairs . Here, the key is a constant that defines the data set (e.g., gender, color, region), and the value is a variable that belongs to that set. A simple example of a key-value pair is “color = blue.”

Key-value stores are designed for high performance and low latency. They are ideal for accessing data quickly and frequently. They are used for caching, session management, and real-time analytics.

Some key-value stores serve other use cases by allowing complex data structures to be stored as values, such as lists, sets, or maps.

Examples of popular key-value stores include Redis, Amazon DynamoDB, and Riak.

Column-family databases

Column-family databases, also known as column-oriented databases or wide-column stores, store data in columns instead of rows. 

A column-family database system organizes data by column families or groups of related columns. They are highly scalable and optimized for read-heavy workloads, making them ideal for data analytics and reporting.

Apache Cassandra, Google Bigtable, and ScyllaDB are examples of column-oriented databases. They are used for real-time analytics, IoT data processing, and content management.

Graph databases

A graph database stores data in a graph-like structure consisting of nodes (vertices) and edges (relationships). They represent and store complex relationships between data points. 

Users can leverage built-in visualization tools within a graph database to explore and understand the relationships between data points.

A graph database can provide high performance for complex queries, as they can navigate through large datasets and query relationships between nodes.

Examples of graph databases include Neo4j, Amazon Neptune, and OrientDB. These databases are used in social networks, recommendation engines, and fraud detection.

Time-Series Databases

Time-series databases (TSDB) store and query time-stamped or time-series data. This data type is characterized by measurements that are tracked, monitored, and aggregated over time. 

Sensor data, stock prices, and server logs are examples of time-series data. TSDB solutions are used for IoT sensor networks, financial analysis, and log management.

Time-series databases utilize data retention mechanisms, which allow users to control how long data is retained. Data retention policies can be configured at various levels of granularity and customized to match specific use cases and data storage requirements.

1. Efficient storage and retrieval of time-series data:  Time-series databases efficiently store and analyze data based on time intervals. They are optimized for fast data ingestion and retrieval, allowing for real-time analysis of data streams.

2. Time-based aggregations and computations: This type of database includes built-in support for aggregation and analytics functions, making it easy to perform complex calculations and analysis on time-series data.

Popular time-series databases

1. InfluxDB: InfluxDB is an open-source, distributed time-series database. It can handle high write and query loads for large-scale time-series data. It supports use cases like monitoring, IoT, and real-time analytics, where time is a critical factor in data analysis.

2. TimescaleDB: TimescaleDB is an open-source, relational database built as an extension of PostgreSQL. It adds time-series-specific features on top of PostgreSQL’s capabilities.

3. OpenTSDB: OpenTSDB (Open Time Series Database) is a distributed database for storing high volumes of time-series data. It is built on the Hadoop Distributed File System (HDFS) and HBase, which are part of the Apache Hadoop ecosystem. It can be used as a standalone database or as part of a larger data pipeline.

NewSQL Databases

NewSQL databases are designed to leverage the benefits of both SQL (relational) and NoSQL databases. They combine the scalability and performance of non-relational databases with the familiar structure and querying capabilities of SQL databases.

These databases use distributed architectures and clustering to achieve high scalability while still providing the strong consistency guarantees and transactional capabilities of traditional relational databases. 

NewSQL databases use horizontal scaling and support distributed transactions, enabling users to perform complex transactions across a distributed environment.

1. ACID compliance: NewSQL databases maintain ACID compliance, ensuring data consistency and integrity.

2. Scalability and performance enhancements: NewSQL databases provide high performance and low latency. They are horizontally scalable and can handle massive amounts of data and traffic.

Popular NewSQL databases

1. CockroachDB: CockroachDB is an open-source, distributed SQL database that uses a “geo-partitioning” approach to achieve high scalability and availability.

2. Google Cloud Spanner: Google Cloud Spanner is a fully managed, horizontally scalable relational database designed to provide strong consistency and transactional capabilities across a global network of data centers.

3. MemSQL: MemSQL is a distributed, in-memory SQL database that provides real-time analytics and transactional processing on large volumes of data.

In-Memory Databases

In-memory databases store data entirely in the main memory ( RAM ) of a computer instead of on disk or other secondary storage devices. This enables rapid data access and query processing, along with improving scalability and availability.

These types of databases are used in real-time applications that require very fast query response times, such as analytics, financial trading systems, and online transaction processing (OLTP) systems.

In-memory databases often have limited capacity compared to disk-based databases. As a result, they may not be suitable for very large datasets or applications with high data ingestion rates.

1. High-speed data access: Since data is stored in memory, in-memory databases can retrieve and process data much faster than disk-based databases, resulting in lower latency.

2. Volatility considerations: In-memory databases mitigate the risk of data loss, due to system crashes, power outages, or other unforeseen circumstances, by using techniques like data replication, snapshotting, and transaction logging.

Popular in-memory databases

1. Redis: An in-memory data store that supports various data structures, like strings, hashes, lists, sets, and sorted sets, and provides advanced features such as transactions and pub/sub messaging.

2. SAP HANA: A high-performance, in-memory database optimized for business analytics and real-time data processing.

3. Memcached: Memcached is a distributed memory caching system. It is used to cache frequently accessed data such as HTML pages, images, and database query results. This speeds up web applications by alleviating database load.

Distributed Databases

A distributed database is a database that is spread across multiple nodes or locations, connected through a shared network. It is managed using a distributed database management system (DDBMS). 

Data storage in a distributed database is done using two methods - data replication and fragmentation.

They are built to address the limitations of a traditional centralized database, such as scalability, availability, and fault tolerance.

1. Horizontal scaling: Distributed databases can scale horizontally by adding more nodes, which helps them handle large data volumes and high traffic levels.

2. Partitioning: A distributed database must continue functioning even when individual nodes or sub-networks fail or become unavailable. They must handle network partitions and ensure that data remains consistent.

3. Fault tolerance and high availability: This type of database is highly available and resilient, even in the face of hardware or network failures. Data is typically replicated across multiple nodes to ensure that users can always access data.

4. Performance and consistency: Distributed databases deliver high performance using optimized data structures and techniques like sharding and indexing. They also ensure that all nodes have consistent data using consensus algorithms like Paxos or Raft.

Popular distributed databases

1. Apache Cassandra: Apache Cassandra is a distributed database management system that stores data across many commodity servers. It uses a column-oriented data model and offers high availability, tunable consistency, linear scalability, and support for multi-data center replication.

2. Amazon DynamoDB: Amazon DynamoDB is a fully managed NoSQL database for modern applications that require consistent, single-digit millisecond response times at any scale. It is a serverless database that automatically scales tables based on storage requirements. It supports both document and key-value data models.

3. CockroachDB: CockroachDB is an open-source, distributed SQL database that can run at scale, spanning multiple data centers and cloud regions. It is a cloud database that offers horizontal scaling, automatic failover, and self-healing capabilities.

Other databases

There are two other common types of database systems to consider - hierarchical databases and object-oriented databases. These databases are not widely used today and serve specialized use cases.

A hierarchical database stores data using a tree-like structure. Data is stored as records and represented by a node. The top node is called the root node. All other nodes are connected to it in a hierarchical fashion using parent and child nodes. Each parent node can have one or more child nodes, but each child node has only one parent node.

Hierarchical databases are not suitable for more complex relationships between data. Changing the database schema and data elements can also be difficult in a hierarchical model.

A database where a child node can have more than one parent node is called a Network Database. This database system is highly structurally dependent, so altering the structure is challenging.

An object oriented database management system (OODBMS) stores database entries in the form of objects , which are instances of classes or prototypes. Developers can use an object oriented programming language to manage data. 

Data objects and their properties stored in an object oriented database persist even after your program terminates.

Object oriented databases support OOP concepts and programming paradigms, including encapsulation, inheritance, abstraction, and polymorphism.

Object oriented databases support many data types, but the highly complex data structure can impact performance. Integration with other systems, like business intelligence tools, can also be challenging.

Choosing the Right Database for Your Project

Consider these four significant factors when selecting a database for your project: 

1. Data model and structure

The first consideration should be the data models and structure of each type of database system. These two factors affect key project requirements, including data types, data volumes, methods used to access data, query types, and query response times.

Relational databases are best suited for structured data with well-defined relationships between entities. They have a fixed data model and can perform complex analytical queries. 

NoSQL and other distributed databases are ideal for unstructured or semi-structured data . They have flexible data models, provide high performance, and enable data engineers to make database changes quickly.

NewSQL databases are also a great option, provided the project is not too complex and does not require advanced features like stored procedures or triggers.

2. Scalability requirements

If scalability is a crucial requirement, distributed and NoSQL databases are generally a better option because they can scale horizontally across multiple nodes. Other types of database structures, like SQL databases, can be more expensive and resource-intensive to scale

NewSQL databases are also highly scalable but may fall behind NoSQL databases for very large or complex data sets.

3. Consistency and reliability needs

Evaluate the level of consistency required for the data stored and whether eventual consistency is acceptable.

Traditional relational databases, like MySQL or PostgreSQL work when your project needs strong consistency and data integrity. However, if eventual consistency is acceptable, then a NoSQL database such as Cassandra vs. MongoDB is a good option.

4. Budget and resource constraints

Consider the cost of licensing, hosting, and maintenance for each database option. Traditional RDBMS systems might be cheaper to set up initially, but the costs of scaling and additional tools for modern data integration can add to expenses. 

Enterprise database management systems typically offer the most features but can be expensive. Most data management software vendors offer custom pricing for enterprise database solutions.

Some databases also require expensive licensing fees and hardware requirements, which may not be feasible for smaller projects or organizations with limited resources. 

Other database management systems may require specialized knowledge or expertise to operate. This leads to additional costs for hiring skilled personnel or outsourcing the task.

Cloud databases and open-source data management tools are the most cost-effective options for current data teams. They eliminate the need for expensive hardware and offer pay-as-you-go pricing models. 

Balancing trade-offs and making informed decisions

Data teams must carefully assess available database management systems to make informed decisions and create an effective and dynamic data environment. Balancing out the pros and cons of each type of database is essential to finding a solution that meets project and team needs.

For example, a NewSQL database system can mitigate the trade-off between the performance and scalability considerations of an RDBMS and a NoSQL database. However, it is a relatively new technology that comes with its own cons.

Ultimately, the goal is to choose a database where the strengths cater to the project’s specific purpose and the weaknesses are not significant. This requires some experimentation and iteration to find the best fit.

The types of database management systems are growing with the advent of new technologies.

Most developers and engineers are familiar with RDBMS, but a combination of newer database systems, like distributed and NewSQL databases, can also be used to reach their goals. 

The accurate analysis of critical factors like data size and growth projections, expected query volume and complexity, and consistency requirements help data teams pick the best type of database for their needs.

Experimenting and ongoing monitoring are also necessary to ensure that the database meets the organization’s evolving needs over time. If you're eager to expand your knowledge, delve into our comprehensive article on Data Partitioning for in-depth insights.

You can read our blog to learn more about data engineering and how to garner valuable data insights.

About the Author

Aditi Prakash is an experienced B2B SaaS writer who has specialized in data engineering, data integration, ELT and ETL best practices for industry-leading companies since 2021.

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What Is A Database Model? (Definition and Examples)

If you’re like me and you’re learning how to design a database, then you need to know what a database model is. I just finished my introductory database design course at my University, so I know a bit about it. Below I describe exactly what a database model is and give a few examples so you know which database model to choose for your project .

What Is A Database Model?

A database model refers to the structure of a database and determines how the data within the database can be organized and manipulated. There are several types of database models including the relational model, the hierarchical model, the network model, the object-oriented model, and more. The most common database model today is the relational model.

Relational Database (The Most Common Model)

A relational database model is the most commonly used model when constructing or redesigning a database. The relational model consists of multiple tables that bear some relationship with each other. Each table contains attributes that are the key that forms these relationships.

For example, consider the tables below:

There is a table for Students that contains attributes for their student ID, their first and last name, and their email.

There is another table for Classes that contains attributes for the class ID, the class name, and the professor for the class.

Last, there is a table for the Registered Classes which has a register ID, term, and two attributes taken from the previous two tables: student ID, and class ID. This represents how these tables are related to each other and thus, how relational models are structured.

Advantages of Relational Model

• Easy to use

Disadvantages of Relational Model

• Slow extraction
• High memory consumption

Hierarchical Database Model (Least Common)

The hierarchical database model is a very structured top-down way of organizing data. That’s to say that the data in this model is organized in a tree-like structure with the top of the tree being the top of the hierarchy.

For example, consider the hierarchical model of a University:

The hierarchical database model was popular in the early days of the digital database in the 1950s and 1960s as people transitioned from the paper filing of data. That’s why hierarchical databases are organized in the same fashion as a filing cabinet. However, it’s not a commonly used database model anymore.

Advantages of Hierarchical Model

• Easy addition and deletion of data
• Relates to natural hierarchies
• Supports one-to-many relationships

Disadvantages of Hierarchical Model

• Not scalable
• Not flexible
• Difficult to Query
• Slow to search
• Prone to anomalies
• Doesn’t support many-to-many relationships

Network Database Model

The network database model is similar to the hierarchical model. It was introduced in the late 1960s as a response to the inefficiencies of the hierarchical model. The major inefficiency solved by the network model was the many-to-many relationships that allowed for faster searches. This new efficiency was crucial for businesses.

For example, let’s look at the network model for a store:

A store has a manager, salespeople, and customers. The store is at the top of the structure because it encompasses the other data elements. The store is a parent element to customer, manager, and salespeople. Additionally, anyone of the three can make an order but only salespeople have access to the stores items.

Advantages of Network Model

• Easy to understand
• Business compatible

Disadvantages of Network Model

• Inefficient
• Difficult to modify

Object-Oriented Database Model

The object-oriented database (OODB) model is similar to the relational model in that various tables represent real-life objects. However, similar or object-oriented programming (OOP) instances of objects can be created within the database.

Consider the following example of a student object:

The student object has four attributes, similar to the first example: a student ID, first and last name, and a student email address. This object acts as a template when creating instances of the object. The object instances are digital representations of a real-life object.

Additionally, unlike the relational model, the OODB model also supports data such as images. Considering these differences, OODB is often referred to as a hybrid model.

Entity-Relationship Model

The entity-relationship database model is similar to the network model because it shows the relationship between two entities. However, the entity-relationship model or more detailed and allows for additional types of relationships, known as cardinality.

To be specific, these models can have one-to-one, one-to-many, or many-to-many relationship types. How the entities are related is also specified in the entity-relationship model.

Let’s look at the following entity-relationship:

This shows two entities, a student and a class. Entities are represented with a rectangle and the type of their relationship is represented with a diamond. The type of relationship will always be between the two entities.

Class and student also have a many-to-many cardinality represented by the ‘m’ and ‘n’ next to the entities. This represents that many students can take many classes at once.

Advantages of Entity-Relationship Model

• Great visual representation
• Simple to conceptualize
• Integrate with relational or other data models

Disadvantages of Entity-Relationship Model

• No industry standard for notation
• Data manipulation not represented

Which Database Model Is The Best?

Before choosing the model for your project, it will help to be clear on what exactly your building:

• How large is your project?
• How important is speed?
• How will the project scale?
• Will there be future enhancements?

No single database model will be the best option in every scenario. However, the relational database model is the most common model and meets most needs. In order to determine which model will work best for you, consider the advantages and disadvantages of each.

Tim Statler

Tim Statler is a Computer Science student at Governors State University and the creator of Comp Sci Central. He lives in Crete, IL with his wife, Stefanie, and their cats, Beyoncé and Monte. When he's not studying or writing for Comp Sci Central, he's probably just hanging out or making some delicious food.

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In the previous tutorial you learnt to install SQL on your device. Now, let's learn about SQL and databases.

• Introduction to Databases

A database is an organized collection of data.

• Types of Databases

In general, there are two common types of databases:

• Non-Relational
• Non-Relational Database

In a non-relational database, data is stored in key-value pairs. For example:

Here, customers' data are stored in key-value pairs.

Commonly used non-relational database management systems (Non-RDBMS) are MongoDB, Amazon DynamoDB, Redis, etc.

• Relational Database

In a relational database, data is stored in tabular format. For example,

Here, customers is a table inside the database.

The first row is the attributes of the table. Each row after that contains the data of a customer.

In a relational database, two or more tables may be related to each other. Hence the term " Relational ". For example,

Here, orders and customers are related through customer_id .

Commonly used relational database management systems (RDBMS) are MySQL, PostgreSQL, MSSQL, Oracle etc.

Note : To access data from these relational databases, SQL (Structured Query Language) is used.

• Introduction to SQL

Structured Query Language (SQL) is a standard query language that is used to work with relational databases.

We use SQL to perform CRUD (create, read, update, and delete) operations on relational databases.

• Create: create databases or tables in a database
• Read: read data from a table
• Update: insert or update data in a table
• Delete: delete tables or databases
• SQL Example: Read Data From a Table

Here, this SQL command selects the first name and last name of all customers from the Customers table using the SQL SELECT statement.

SQL is used in all relational databases such as MySQL, Oracle, MSSQL, PostgreSQL etc.

Note : The major SQL commands are similar in all relational databases. However, in some cases, SQL commands may differ.

In this SQL tutorial series, we will learn about SQL in detail. We will cover any SQL command differences among MySQL, Oracle, SQL Server, Postgres, and other commonly used database systems.

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What Is a Database Management System (DBMS)?

A database management system (DBMS) describes a collection of multiple software services that work together to store, compute, maintain, structure, and deliver the data as part of a product. This platform also provides metadata, a system of data labeling, so that engineers and users can understand and map what entities and properties are available and their […]

A database management system (DBMS) describes a collection of multiple software services that work together to store, compute, maintain, structure, and deliver the data as part of a product. This platform also provides metadata, a system of  data labeling , so that engineers and users can understand and map what entities and properties are available and their relationships.

Using the DBMS’s metadata, engineers create, track the activity of, and delete users in the managed databases. Moreover, engineers use the metadata in this management platform to configure data access and enforce security, helping organizations comply with regulations and protect sensitive information.

While database administration products offer  structured ways  to organize, manage, and protect data, enforce data integrity, and facilitate data sharing and collaboration, they vary based on the underlying technologies and their offerings. For example, a  relational database system  (RDBMS), like Microsoft SQL Server, ensures reliable database transactions through  ACID  properties. On the other hand, a  non-relational database system  (NRDBMS or No-SQL DBMS), like MongoDB, scales data better and handles rapid data changes and scales better through  BASE  properties.

Consequently, each DBMS has unique strengths and weaknesses, offering various tradeoffs. Using a  Data Management framework to plan and do activities on the DBMS ensures organizations get the most out of features that add, store, and ensure quality data during integration. The framework and database management must synchronize to handle specific business needs and encourage best practices.

Database Management System Defined

When discussing database management systems, businesspeople often refer to the  physical product  that enables data storage, organization, and defined formats and structures. This Data Management application can be implemented as a cloud-based or on-premises platform. Regardless of its deployment, database management encompasses the technical, only a piece of database management.

The larger database management system comes under the broader concept of Data Management, which includes processes and roles that make systems run well. For example, while many DBMSs have automated data cleaning tools, this feature alone does not improve  Data Quality . Instead, business planning, guidance, and activities are critical in aligning DBMS results with the business strategy.

From a technical perspective, a DBMS emphasizes administration functionalities. BMC, for example,  describes it as  a software tool “used to manage a database easily.” The info lab at  Standard  highlights how the DBMS manages large data volumes and supports access to this information. 

When viewed as a Data Management component, DBMS is a tool to synchronize with business goals. It  coordinates with database resources as users gather, process, and analyze the data it stores.  Splunk , a leading company providing database functionality, compares the DBMS to an electronic filing cabinet that efficiently holds, organizes, and retrieves large amounts of data.

Why Is a Database Management System Important?

A database management system coordinates technical resources to perform various business and customer tasks that require databases. For example, the DBMS offers functionalities that enable customers to search for merchandise, obtain information about it, and make purchases. Moreover, a DBMS can handle multiple user requests to interact with the data and provide functionality for businesses to scale their operations. 

Additionally, the DBMS has features administrators use when doing maintenance. These activities include backup, tuning, importing, repairing, indexing, or exporting data to store and retrieve information efficiently.

Automation and  machine learning  (ML) features of a DBMS contribute to improved performance and adaptability in unexpected situations, such as accidental data deletion. In such cases, the database management system can restore functionality based on the engineer’s recommendations and prevent data corruption that could make databases unusable.  

Furthermore, the technical functions of a DBMS is vital in maintaining data integrity during use. For instance, the database notifies a user inputting data that a parameter cannot remain empty and requires a value, such as a customer’s first and last name. The DBMS can also prevent duplication of data entry through its services.

DBMS Benefits

Database management benefits businesses by giving them the capabilities to handle a greater volume of transactions more quickly. These advantages come to fruition with a solid Data Management foundation, including a Data Strategy, Governance, and Architecture guiding the technical database activities.

Corporations run operations using database management systems, like inventory tracking and handling customer relationships. Database automation can improve the performance of data operations, and a DBMS saves paper once needed to do complex calculations or keep track of products.

For these reasons, the DBMS is ever-present in daily life. Banks, like Wells Fargo, use databases to keep track of accounts and transfer funds. Stores like Amazon use databases to locate, restock, and sell items. There’s a DBMS almost behind every web app, from messaging, ride-sharing, and searching the web to playing a game with friends.

Most importantly, companies need at least one DBMS to show compliance with data regulations by helping engineers provide  data lineage  or the history of the data’s journey. Organizations use these platforms to ensure access to get tasks done and provide security that protects privacy and prevents user confusion with irrelevant information.

DBMS Abstraction

When teams discuss database management systems, they often plan and design system components to develop or improve new database functionality.

So, a group may represent one or multiple components of a DBMS in an abstract form using a  data model . This component takes on a conceptual, logical, or physical abstraction, typically sketches and descriptions. Each type provides a different perspective on a particular DBMS module and its integration with other entities. 

A conceptual model allows a business to understand the functionality of a DBMS element by visualizing entities, attributes, and relationships. A different type, a physical model, focuses on explaining how to build the solution. Finally, a logical model describes how the DBMS components function together based on rules and data structures.

All the DBMS diagrams need to synchronize with each other. That way, the technology team builds the components as specified, and the resulting architecture meets business requirements.

Different Types of Database Management Systems

Database management systems incorporate various components to store, process, administer, and deliver data as a cohesive application. These DBMSs can be categorized into distinct types:

• Centralized DBMS: This type of system employs a  centralized Data Architecture , where all the data resides in one system and serves up that information to users from there.
• Distributed DBMS: These systems utilize a distributed Data Architecture. The data is spread across multiple systems or nodes, enabling fast access and serving as a failsafe through redundant data storage.
• Federated DBMS: A federated arrangement consists of multiple databases that may have a mix of centralized or distributed architectures. It uses a  data virtualization  technique to disparate systems data into a unified view.  It achieves this without duplicating or persisting the source data, thereby preserving data integrity.

Federated DBMS components function as:

• Loosely Coupled:  Component databases create federated schemas and typically require accessing other component database systems through a multi-database language.
• Tightly Coupled: Component systems employ independent processes to construct and publish one integrated federal schema.
• Blockchain DBMS: The  Blockchain  DBMS combines centralized and distributed DBMS elements to get agreement on a ledger. If the ledger is compromised, the blockchain system rejects it. Blockchain databases consist of individual records and blocks that employ cryptography to safeguard the data. 

These different types of DBMS offer varying architectures and capabilities that can be best chosen and used through applying a Data Management framework.

DBMS Components

A DBMS consists of various components that work together to manage data. They include:

• Standalone, client-server, on-premises: In this configuration, the DBMS resides on a single computer, and the data is stored there as well. Typically, one person uses the DBMS for database management and interfaces with the data through a client as an end-user on the same machine. This setup offers easier control over Data Quality.
• Networked client-server, on-premises: This  configuration  involves one or more server machines and multiple client computers. Technical staff uses the DBMS for administration, while businesspeople, the end users, interact with the data through client workstations. Data Quality becomes more complex in this scenario.
• Cloud database: In this case, a vendor administers DBMS, and the results appear “invisible” to users in an organization. The level of database management through the DBMS depends on the contract between the vendors and the company. Data Quality can become quite complex since data is handled internally and through a third party.

Software: DBMS software comprises a collection of Data Management applications and instructions running on the machines through the DBMS. Some examples include:

• Databases: They are programs that  hold data  in a structured and organized manner. Various databases exist, including  flat-file, relational, non-relational, No-SQL, and graph. Newer databases specialize in storing vectors, enabling AI engines to find similarities and patterns quickly.
• Tools:  Tools enhance DBMS functionality to manage data more efficiently and elegantly and are customizable. Technical and business users can take advantage of different tools. Middleware: Middleware bridges database administration and the user interface. Middleware supports more in-depth analysis through batch processing or real-time processing. An in-memory database, a type of middleware, combines hardware and software for fast access.
• User Interface: A user interface is one of the most visible parts of a database management system, enabling all members of an organization to interact with the data there. It allows users to find, view, configure, and analyze data. 

Hardware, software, tools, and middleware DBMS components support database use, working invisibly behind the user interface.

Image credit: Shutterstock

• Mastering Database Assignments: Your Comprehensive Guide

Navigating Database Assignments: A Step-by-Step Guide for Success

Embarking on the journey of database assignments is a dynamic venture that presents both challenges and rewards. Regardless of whether you find yourself navigating the academic realm as a student or seeking to elevate your professional expertise, this comprehensive guide serves as an invaluable companion throughout the entire process. From laying the groundwork by understanding fundamental concepts to the practical application of UML diagrams in database design, this guide is crafted to provide a seamless and insightful experience. As you progress, the guide will aid in deciphering complex assignment instructions, establishing a strategic framework, and delving into the principles of database design. With a spotlight on essential aspects like normalization techniques and relationship mapping, you'll gain a nuanced understanding of structuring databases for optimal performance. The journey further unfolds into the practical implementation phase, where you'll delve into the intricacies of writing SQL queries and employing data modeling techniques with tools like MySQL Workbench. The guide extends its support into troubleshooting common issues and optimizing database performance, ensuring a well-rounded comprehension of the entire database assignment landscape. Testing and validation, crucial components of the process, are explored extensively, emphasizing rigorous testing protocols and the importance of user feedback for iterative improvement. Whether you're a novice seeking to grasp the basics or a seasoned professional aiming to refine your skills, this guide is tailored to offer actionable insights and tips at each juncture. As you navigate the intricate world of database assignments, this guide stands as a beacon, illuminating the path to success with its comprehensive approach, ensuring that you emerge well-equipped and confident in your ability to tackle any database assignment that comes your way.

The guide encourages a proactive mindset, fostering an understanding that every database assignment is an opportunity for growth and skill refinement. It recognizes the importance of aligning theoretical knowledge with practical implementation, emphasizing the mastery of SQL queries, data modeling techniques, and troubleshooting strategies. By unraveling the complexities of common issues that may arise during assignments, such as schema errors and performance challenges, the guide empowers you to approach problem-solving with confidence and precision. Furthermore, it underscores the significance of performance optimization strategies, from indexing to query optimization, ensuring that your database not only meets the assignment requirements but operates at peak efficiency. As the journey concludes, the focus shifts to testing and validation, guiding you through a comprehensive testing strategy that encompasses unit testing, integration testing, and validation against real-world scenarios. The iterative improvement process is highlighted, recognizing the value of user feedback in refining your database design to meet evolving requirements.

Are you struggling to solve your Database homework ? This guide encapsulates the entire spectrum of navigating database assignments. Whether you are entering this realm with curiosity or experience, the guide serves as a reliable companion, providing practical wisdom and insights that transcend the theoretical. By the time you reach the conclusion, you'll find yourself well-versed in the intricacies of database assignments, armed with the knowledge to tackle challenges and contribute meaningfully to the dynamic field of database management. The journey, though demanding, is undeniably rewarding, and this comprehensive guide ensures that you traverse it with competence, resilience, and a deep understanding of the intricate world of databases.

Understanding the Basics

Before embarking on database assignments, establishing a robust foundation in database fundamentals is imperative. This involves delving into essential concepts like data models, relational databases, and normalization. A thorough grasp of these fundamentals not only facilitates a deeper understanding of database structures but also serves as the cornerstone for successful assignment completion. Additionally, recognizing the pivotal role of Unified Modeling Language (UML) diagrams is essential in the realm of database assignments. These diagrams, particularly entity-relationship diagrams (ERDs), hold significant weight in visualizing and conceptualizing database structures. Learning to create UML diagrams enables effective communication of database designs and contributes to a clearer representation of relationships among data entities. In essence, the synergy between comprehending database fundamentals and harnessing the power of UML diagrams sets the stage for a more informed and structured approach to tackling intricate database assignments.

Moreover, a nuanced understanding of data models lays the groundwork for effective communication between stakeholders involved in the assignment process. By comprehending the intricacies of relational databases, individuals can navigate the complexities of data storage and retrieval, essential components of any successful database assignment. The significance of normalization, a process to eliminate data redundancy and ensure data integrity, cannot be overstated. It establishes the guidelines for organizing data efficiently, contributing to the overall effectiveness of a database system.

Simultaneously, delving into the importance of UML diagrams unveils a visual language that transcends the limitations of text-based explanations. ERDs, a specific type of UML diagram, provide a graphical representation of entities and their relationships, offering a holistic view of the database structure. Proficiency in creating UML diagrams empowers individuals to convey complex database designs in a comprehensible manner, fostering collaboration and understanding among team members.

Analyzing Assignment Requirements

Embarking on a database assignment demands the twin capabilities of decoding assignment instructions and establishing a robust framework, both integral to ensuring triumph in the intricate landscape of database design. In the decoding phase, a meticulous breakdown of instructions is paramount – an analytical dissection where key requirements, constraints, and objectives are identified. This process, akin to deciphering a complex code, is the keystone for comprehending the assignment's scope, laying the foundation for subsequent strategic decisions. Simultaneously, the establishment of a framework involves the creation of a comprehensive roadmap. This entails defining the assignment's scope, functionalities, and data entities, fostering a structured approach that transforms the assignment into a navigable journey. These dual processes, decoding and establishing, synergistically shape the entire trajectory of the database assignment, guaranteeing not just completion, but success through clarity, coherence, and operational efficiency in every phase of the intricate database design process

Beyond being procedural necessities, decoding assignment instructions and establishing a framework serve as proactive measures that significantly impact the overall quality of the database solution. By meticulously decoding instructions, one gains a nuanced understanding of the assignment's nuances, fostering an awareness that goes beyond the surface requirements. This depth of comprehension becomes the fulcrum upon which creative and innovative solutions can be built. Similarly, the framework-setting phase is not merely a logistical exercise; it is a strategic endeavor that shapes the assignment's trajectory. The defined scope becomes a boundary for creativity, functionalities are crafted with purpose, and data entities are chosen with foresight. This intentional approach ensures that every subsequent step aligns with the overarching objectives, preventing missteps and ensuring a cohesive, well-integrated database design.

Moreover, the iterative nature of these processes becomes apparent as the assignment progresses. As challenges emerge, the initial decoding of instructions provides a reference point, enabling dynamic adjustments to the evolving understanding of the assignment. The established framework serves as a flexible guide, allowing for adaptations and refinements based on newfound insights or changing requirements. In essence, decoding instructions and establishing a framework are not isolated actions; they are continuous threads woven into the fabric of the entire database assignment lifecycle.

Database Design Principles

Delve into the critical aspects of database design with a focus on normalization techniques and relationship mapping. Normalization serves as a fundamental principle in eliminating data redundancy, promoting a well-organized database structure. In this section, gain insights into the various normal forms and learn how to apply them judiciously to enhance data integrity. Uncover the intricacies of relationship mapping, where you'll master the art of defining connections between entities. Understand the nuances of one-to-one, one-to-many, and many-to-many relationships, pivotal for designing a database that accurately mirrors real-world scenarios. This exploration ensures not only the efficiency of your database but also its alignment with the complexities of the environments it aims to represent.

As you navigate normalization, consider the journey towards a database that not only stores data but does so with optimal efficiency and accuracy. Normalization not only streamlines your data structure but also minimizes the chances of anomalies, ensuring that your database remains a reliable source of information. Additionally, the mastery of relationship mapping goes beyond theoretical knowledge, empowering you to translate real-world connections into a digital format seamlessly. By understanding the dynamics of different relationships, you pave the way for a database that not only functions well internally but also accurately represents the intricate web of connections found in diverse scenarios. This dual focus on normalization and relationship mapping is the cornerstone of building databases that stand the test of practical implementation and real-world demands.

Writing SQL Queries: Navigating the World of Structured Query Language (SQL)

Embark on an enriching journey into the dynamic realm of SQL (Structured Query Language), the heartbeat of seamless database interactions. This section serves as your comprehensive guide to mastering the intricacies of SQL, providing you with the adept skills needed to navigate databases with finesse. Beginning with the fundamentals of SELECT statements and advancing into the intricacies of JOIN operations, you will develop the proficiency required to retrieve and manipulate data with surgical precision. Beyond being a mere skill, SQL proficiency emerges as the bedrock of successful database management, bestowing upon you the power to unlock and leverage the full potential of data within your assignments. Embrace SQL mastery, and chart a course toward elevated excellence in the realm of database dynamics..

Data Modeling Techniques: Crafting Intuitive Database Designs

In this segment, we explore the art and science of practical data modeling techniques, transforming abstract ideas into tangible, well-designed databases. Employing tools such as MySQL Workbench or Oracle SQL Developer, you'll gain hands-on experience in visualizing your database architecture. Visualization is not only about creating aesthetically pleasing diagrams; it's a strategic approach to conceptualizing the relationships between data entities. As you delve into data modeling, you'll discover its pivotal role in ensuring your database design aligns seamlessly with user feedback and evolving project requirements. These techniques are the bedrock of creating databases that not only meet specifications but also adapt and evolve with the dynamic nature of real-world applications.

Troubleshooting and Optimization

Navigating the intricate realm of database assignments demands a meticulous understanding of potential challenges and the adept application of optimization strategies. Beyond merely identifying schema errors and performance bottlenecks, this section of the guide immerses you in a deeper exploration of solutions that go beyond the conventional. Gain insights into advanced indexing techniques that go beyond the basics, strategically enhancing the efficiency of your database. Uncover the art of query optimization, a nuanced skill that distinguishes a proficient database designer from the rest. By honing these techniques, you not only mitigate risks but also elevate the overall performance of your database to unprecedented levels.

This comprehensive approach not only addresses common issues but fosters a strategic mindset towards problem-solving. It emphasizes the symbiotic relationship between potential challenges and optimization, reinforcing your ability to design databases that stand resilient against the complexities of real-world scenarios. In mastering this segment, you not only troubleshoot effectively but cultivate an expertise that transcends the ordinary, positioning yourself as a proficient navigator in the ever-evolving landscape of database assignments.

Rigorous Testing Protocols

In the realm of database assignments, the significance of implementing rigorous testing protocols cannot be overstated. A robust testing strategy serves as the linchpin for ensuring the flawless functionality and unwavering reliability of your database. This involves a meticulous approach to various testing methodologies, including the critical steps of unit testing, where individual components are scrutinized for accuracy, integrity, and functionality. Integration testing becomes equally pivotal, examining the seamless interaction between different components to ensure their harmonious collaboration within the larger system. Yet, the testing journey doesn't conclude there; it extends to the validation against real-world scenarios, where the practical application of the database is scrutinized under diverse conditions. Each testing phase contributes to fortifying the database's resilience, identifying potential vulnerabilities, and refining its performance. In essence, a well-executed testing protocol is the bedrock upon which a robust and dependable database stands.

User Feedback and Iterative Improvement

The symbiotic relationship between user feedback and iterative improvement is a cornerstone in the evolutionary process of database assignments. Beyond the realms of coding and design, the incorporation of user perspectives and stakeholder insights becomes paramount. Gathering feedback from end-users provides invaluable insights into the usability and functionality of the database in real-world scenarios. This iterative approach extends beyond mere bug fixes; it entails a profound commitment to continuous improvement. Embracing a mindset that perceives each feedback loop as an opportunity for enhancement, database designers iterate on their designs accordingly. This iterative improvement process is not only about fixing issues but also about adapting to evolving requirements and expectations. It is a dynamic cycle where user feedback fuels design refinements, creating a symbiotic relationship that fosters an ever-improving user experience. In essence, the database becomes a living entity, evolving in response to the needs and experiences of those who interact with it.

In conclusion, successfully navigating the intricate landscape of database assignments demands a harmonious blend of theoretical acumen and hands-on practical skills. This step-by-step guide serves as a beacon, illuminating the path to confidently tackle assignments, thereby ensuring triumph in your ventures within the realm of databases. Offering a holistic perspective, this comprehensive guide delves into the depths of database assignments, guiding you seamlessly from the conceptualization phase to the intricacies of implementation.

Mastering the principles of database design is paramount in establishing a solid foundation for your assignments. It involves understanding data models, normalization techniques, and the art of relationship mapping. With these skills in your arsenal, you can create well-structured databases that accurately represent real-world scenarios, minimizing data redundancy and ensuring data integrity.

Equally important is the proficiency in SQL queries, a powerful language for interacting with databases. From crafting basic SELECT statements to executing complex JOIN operations, acquiring these skills empowers you to retrieve and manipulate data with precision. The guide further extends into the realm of practical implementation, introducing data modeling techniques using tools like MySQL Workbench or Oracle SQL Developer.

Troubleshooting and optimization strategies are indispensable components of the database journey. As you explore common issues and delve into performance optimization techniques, you gain the ability to identify and rectify challenges, ensuring the efficiency and responsiveness of your databases.

Testing and validation emerge as crucial steps in the database assignment lifecycle. Implementing rigorous testing protocols and soliciting user feedback allow you to refine and iterate on your designs. Embracing a continuous improvement mindset positions you to adapt to evolving requirements, contributing to a dynamic and resilient database system.

In essence, each assignment becomes more than a task—it transforms into an opportunity for skill refinement and meaningful contribution to the dynamic field of database management. Armed with this comprehensive guide, you not only navigate the complexities of database assignments but also elevate your academic and professional pursuits, leaving an indelible mark in the ever-evolving landscape of data management.

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What Is a Database Schema? Types, Use Cases, & Examples

Discover the ins and outs of database schemas - from types and use cases to their applications and learn how they organize and structure data.

More downtime, performance bottlenecks, and hindered ROI – this is what a database schema can pull you out from. Of course, that's other than laying the foundation for  efficient database development. So, overlooking the importance of a well-designed database schema can end up leaving you in a not-so-good situation.

Today, managing unstructured data is a major challenge for  95% of businesses . So, whether you’re running a startup looking to revolutionize an industry or a multinational corporation processing terabytes of information daily, the right schema ensures that your data architecture stays responsive and capable of translating raw information into actionable insights.

But how can you ensure to make the best out of database schemas? From the benefits and types of database schemas to their design patterns and applications across different industries, you’ll find all the answers in this easy-to-follow guide.

• What Is a Database Schema?

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A database schema is a  comprehensive blueprint that formally  defines the complete logical structure and organization of data within a  Database Management System (DBMS) . It defines how data is formatted, stored, processed, secured, and accessed among the various structural schema objects like tables, views, indexes, triggers, logical constraints, etc. 

In other words, schema serves as the skeleton and architectural authority governing everything in the database. It provides:

• Effective  querying capabilities for data engineers
• Overall governance of  database policies and standards
• Accurate access control administration by database managers
• Proper technical design of  database schema table and objects by developers
• 6 Benefits of Using Database Schemas

Database schemas are dynamic tool sets that help in many critical operations in Relational Database Management Systems (RDBMS). Let’s take a look at the top 6 database schema benefits.

Data Integrity

Well-constructed database schemas play an important role in maintaining data validity and consistency. They use column types, NOT NULL, and CHECK constraints to validate new data entries. Also, integrity constraints like primary keys, foreign keys, and unique constraints help maintain data accuracy. 

A centralized schema also  addresses potential issues of missing or duplicate information through default values and constraints. This not only guarantees high-quality data but also makes it reliably accessible to all applications.

Database schemas provide  robust data security . They implement roles, views, and permissions to manage who accesses what. The schema can  restrict data exposure and help in auditing critical activities. Even column-level encryption can be set through the schema for extra security.

Documentation

An up-to-date database schema acts as a guide for your database instance. It helps in  long-term maintenance and simplifies the onboarding of new developers . With the schema, it’s easier to troubleshoot issues and plan new developments. It also helps understand the impact of any changes.

Strong database schemas  provide easier and faster data analytics . They organize data storage and define relationships between data elements to streamline queries and reporting. Analytic engines can then join data sources and perform aggregations more efficiently.

A flexible schema lets you  extend features and functions smoothly . This way, you don’t have to perform massive overhauls when developing new applications in database systems. For example, a blog engine can add social sharing or multimedia features without changing existing data structures and set the path for step-by-step improvements.

Database schemas act as  centralized hubs for rules and standards with guidelines for backup, monitoring, and compliance . This is particularly helpful for large organizations as it provides uniform data handling across multiple database instances. Schemas also help in assigning team roles for improved collaboration across departments.

• Types of Database Schemas

Each schema type plays a unique role in the database life cycle. Let’s discuss these roles in detail.

Conceptual Schema

The conceptual database schema is the highest level of abstraction that focuses on  describing the main entities, attributes, and relationships included in the database design.

• It is developed early in the database planning process to capture business requirements and model the overall data landscape broadly.
• Conceptual schemas commonly use  Entity-Relationship Diagrams (ERDs) to visually represent important entities and their attributes, as well as the relationships between those entities.
• ERDs let non-technical business stakeholders and database designers communicate effectively during requirements gathering.
• As the database design lifecycle progresses, the conceptual schemas are refined to solidify requirements before logical and physical design begins.
• The conceptual schema does not include technical details like data types, constraints, storage parameters, etc. It focuses on the important data entities and relationships from a business point of view.

Logical Schema

The logical database schema  adds more technical specifics,  yet it still keeps some of the physical storage and implementation factors abstract.

• It defines the comprehensive logical structure of the database, like detailed data types, keys, field lengths, validation rules, views, indexes, explicit table relationships, joins, and  normalization .
• The schema outlines the database's detailed logical framework.  It also outlines views, indexes, and table relationships and sets parameters for joins and normalization.
• This schema type uses elements like entity-relationship diagrams, normal forms, and set theory to shape the structure and organization of the database. It also shows relationships and dependencies among various components.
• Logical schemas serve as the formal input for physically implementing the database system using DDL statements, table scripts, or declarative ORM frameworks.

Physical Schema

The physical database schema describes  how the database will be materialized at the lowest level above storage media.

• The schema maps database elements like tables, indexes, partitions, files, segments, extents, blocks, nodes, and data types to physical storage components. This bridges the logical and physical aspects of database management.
• The physical schema specifies detailed physical implementation parameters like file names, tablespaces, compression methods, hashing techniques, integrity checks, physical ordering of records, object placement, and more.
• It includes hardware-specific optimizations for response time, throughput, and resource utilization to meet performance goals.
• It is tuned and optimized over time after the system is operational and real production data volumes and access patterns emerge.
• The Role of Estuary Flow In Database Management

Estuary Flow is our real-time ETL tool designed to  redefine your data management approach. Equipped with  streaming SQL and TypeScript capabilities, it seamlessly transfers and transforms data among various databases, cloud-based services, and software applications.

Far from being just a data mover, Estuary Flow focuses on the user experience and provides  advanced controls to maintain data integrity and consistency. It serves as your all-in-one solution for integrating traditional databases with today's hybrid cloud architectures.

10 Key Features Of Estuary Flow

• Universal data formatting:  Our real-time ETL features easily manage a wide range of data formats and structures.
• Stay updated in  real-time :  Incremental data updates give you the most current data, thanks to real-time CDC features.
• Easy data retrieval:  Fetch integrated data effortlessly from different sources through our globally unified schema functionality.
• The bridge to modern data solutions:  Smoothly transition from traditional databases to modern hybrid cloud setups without any complications.
• All-in-one connectivity:  Select from an extensive library of over 200 pre-configured connectors for hassle-free data extraction from various sources.
• Eliminate redundancies:  Automated schema governance and data deduplication features streamline your operations and reduce unnecessary repetition.
• Adaptable to your needs:  Our system architecture supports distributed  Change Data Capture (CDC) at rates up to 7GB/s to adapt to your evolving data requirements.
• A comprehensive view of your customers:  Combine real-time analytics with historical data to better understand customer interactions and improve your customization strategies.
• Uncompromising data security:  Provides multiple layers of security protocols, including encryption and multifactor authentication, to secure your data integration processes.
• Added layers of security:  Additional authentication and authorization measures provide an extra level of protection against unauthorized access, all without sacrificing data quality.
• Types of Database Schema Design Patterns

Database schema design patterns offer a variety of structures, each well-suited for different types of data and usage scenarios. Choosing the correct design pattern can make data storage and retrieval more efficient. Let’s look at 5 common schema design patterns, each with its unique characteristics and applications.

Flat Schemas

A simple flat schema is   a  single table containing all data fields represented as columns . This table stores all data records without any relationships between elements in the schema.

Flat schema  works well for smaller, less complex data sets rather than large interconnected data. Its simplicity provides quicker queries, thanks to the absence of table joins. However, this comes at the cost of data redundancy as all information is stored in a single table which can cause repeated records.

Although flat schemas are easy to implement, their scalability is limited and they can become inefficient for more complex use cases. Nonetheless, they are effective for  simple transactional records or as initial prototypes that can be changed to more sophisticated database models later.

Relational Schemas

The  relational model stands as the most versatile and widely used database schema design. It  organizes data into multiple tables that are both modular and interrelated . This design approach normalizes data and reduces data redundancy as each table represents just one entity.

Relationships between tables are logically established at the schema level through primary and foreign keys. Although the data is normalized, the relational model still lets you recombine data from different tables via joins during queries.

This mix of isolated tables and interconnected relationships lets you easily expand the structure. This means you can  change the schema without major disruptive changes . Existing applications can continue to operate without modification even as new features are added in separate tables. This flexibility makes relational models ideal for structuring complex, interconnected data sets.

Star Schemas

The star schema is a design pattern that helps in  analytic data warehousing and business intelligence tasks . It structures data into a centralized fact table flanked by multiple-dimension tables, forming a star-like configuration.

Fact tables  capture quantifiable events or business metrics like sales orders, shipments, or supply chain activities. On the other hand, dimension tables contain descriptive, contextual data like customer information, product details, and geographic locations.

This division into separate tables for facts and dimensions let star schemas  support rapid queries even across large data sets. The centralized fact table gives quick access to all associated tables  which makes this model particularly efficient for summarizing, aggregating, and analyzing large amounts of historical data.

However, the star schema has limitations. It's not the best choice when it comes to  handling real-time transactional data or complex interrelationships among data points . Its design is most effective for one-to-many relationships between the fact table and its corresponding dimensions.

Snowflake Schemas

The snowflake schema is a variation of the star schema in which  dimension tables are further broken down into sub-dimensions , creating a branching structure that resembles a snowflake. This extends the normalization process to the dimensions themselves.

For example, a Location dimension may be broken down into Country, State, and City sub-dimensions in a snowflake model. The extra normalization  increases analytic flexibility but also involves additional table joins across these hierarchical dimensions.

Snowflake schemas isolate attributes to minimize duplications for better disk space utilization. The branching dimensions provide easy drill-down across multiple data aggregation levels. However, snowflake queries tend to be more complex because of added normalization.

Graph Database Schemas

A graph-oriented database schema  stores data in nodes that directly relate to other nodes through typed relationship edges . This model efficiently represents highly interconnected data found in social networks, knowledge graphs, or IoT device networks.

Since these relationships are encoded directly at the schema level, graph databases  quickly traverse complex networks of densely related nodes across multiple edges . Even as data volumes increase, query performance remains strong when oriented along the graph dimensions.

That said, graph schemas have their limitations. They are  not very efficient at handling highly transactional data  nor can they handle analytics that involve non-graph structures. For scenarios like social networks, fraud detection, and logistics where the linkage of data is a major concern, graph database schemas are ideal.

• Examples of Database Schema Application Across Different Sectors

Database schemas provide the basic architecture for tackling unique  data management needs in different fields. Let’s see how they use appropriate database schemas for their requirements.

eCommerce Website

Here’s how different database schemas are applied in an eCommerce platform:

• Search engine: To power the platform's search capabilities, another NoSQL database could be set up that specializes in text search and provides features like auto-suggestions, and fuzzy matching.
• Business analytics: Star schemas are employed in a data warehouse with historical sales data, website analytics, and customer behavior. These schemas let you quickly and efficiently run complex queries for analytics reports. 
• Order management: A relational database schema handles the core transactional aspects that include tracking customers, orders, inventory, and shipping. The modular, normalized tables make it easy to update information while maintaining data integrity.
• Product catalog and reviews: A schemaless NoSQL database is ideal for the dynamic nature of a product catalog, including varying attributes and user-generated content like reviews and ratings. This lets you easily add new product categories without affecting the existing schema.

Banking Applications

In the banking sector, multiple types of database schemas are used to efficiently manage different financial activities. Let’s take a look at them.

• Customer communication: Unstructured JSON databases are ideal for storing various forms of customer interactions like emails or chat logs. These databases easily adapt to different kinds of data.
• Transaction handling: Graph databases are used for real-time transaction tracking. They connect customers to their accounts and transactions seamlessly for quick and secure processing of banking activities.
• Historical analysis: Data warehouses that store past transaction records use snowflake schemas for categorization by factors like customer demographics or account types. This helps in trend analysis and making informed decisions.

Healthcare Databases

Different database schema types have unique roles in healthcare systems for managing complex medical data:

• Medical standards:  Reference schemas integrate data from different sources using standardized medical codes and terminologies. This makes the data consistent across the board.
• Clinical notes: Hybrid databases mix structured healthcare records with unstructured notes from healthcare providers. This provides a more complete view of a patient's healthcare journey.
• Patient records: Relational database schemas handle crucial healthcare entities like patient information, medications, and treatment history. With its modular, normalized tables, this schema makes it easy to update data while maintaining its integrity.

Customer Relationship Systems (CRM)

Database schemas in CRM systems address multiple business needs:

• Business metrics: Star schemas within a data warehouse aggregate critical business events like sales and customer interactions. This makes the analytical process more straightforward.
• Communication logs: Hybrid database schemas capture both structured CRM information and unstructured data like emails and calls. This makes the database richer and more versatile for analysis.
• Detail-driven analysis: Snowflake schemas refine broader categories like product types into more specific attributes. This supports more detailed analytics and helps businesses better understand their customer base.

Supply Chain Management

In modern supply chains, database schemas are used for:

• Time-based analytics: Temporal databases specialize in analyzing past performance data to make future predictions about shipping and resource management.
• Supply chain elements: Graph databases model connections between suppliers, manufacturing plants, and logistics centers. This helps in better planning and resource allocation.
• Location optimization: Geo-spatial databases plot the entire logistics network on a map. This helps make on-the-spot decisions for transport and distribution for route optimization and cost reductions.

As data scales, so do the intricacies of its functionality and the magnitude of maintenance challenges. This is where and when a database schema becomes crucial for not only handling sector-specific data challenges, but also for better data governance, flexibility, performance, and scalability. 

If you are looking for a cutting-edge tool to help you in your data management, go for  Estuary Flow . It not only offers real-time database replication but also caters to a range of data needs. This makes Flow a must-have for your data management toolkit.

Sign up for free and start your journey towards efficient, real-time database management today.  Contact our team for more details.

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4 Types of Database Management Systems for Your Small Business

Bhavya Aggarwal

What is a database management system?

1. relational database management system, 2. object-oriented database management system, 3. hierarchical database management system, 4. network database management system.

• Get the perfect database management system

Learn about the different types of database management systems and how they function.

As a small-business leader, do you struggle to manage your company’s database because of limited experience in IT management? You already know that you can use a database management system (DBMS) to tackle this issue, but do you know which type of DBMS would make the most sense for your small-business needs?

Database management systems are usually of four types—each with its unique characteristics, functionality, and purpose. Investing in the wrong type can lead to challenges such as security compliance risks, poor data organization, and excessive data redundancy. 

On the other hand, a good understanding of each DBMS type and how it works can help you select a platform that makes data management easier, eliminates data redundancy, and prevents compliance risks for your business. 

This article explains the four common types of database management systems and their benefits to help you select DBMS software that’s compatible with your existing applications and IT infrastructure.

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A database management system is a software platform that helps store, organize, and retrieve data efficiently. It ensures data accuracy and consistency by enforcing rules such as data constraints that prevent duplication or typos.

Modern database management systems provide an intuitive interface so users can easily navigate complex databases. They come with data security and performance monitoring capabilities as well as backup and recovery mechanisms to help prevent data loss. 

Database management systems support the scalability and growth of your small business by making it easier to handle large volumes of data and new data types. You can also add new data sources, applications, and tables to your databases. 

A relational database management system (RDBMS) stores data in separate tables consisting of rows and columns—much like spreadsheet programs. These tables are linked together using relationships, so you can quickly access, organize, and update the data. 

The relational model stores data in a systematic manner and is well suited to handle a large number of concurrent users and transactions. Let’s look at an example: A public library’s relational DBMS could contain multiple tables such as:

The books table, which stores information about books, including their title, author, genre, and ISBN (a unique number to identify each book). 

The patrons table, which stores information about the library’s patrons, including their name, address, and phone number. 

The transactions table, which stores information about books that are checked out, including the patron ID, book ID, and due date.

These tables are all related to each other and can be used to store and retrieve information about books, patrons, and transactions.

Example of the structure of a relational database management system

Why you should invest in a relational database management system

Easy to use: An RDBMS provides an intuitive interface that you can use to enter into or extract data from your database with minimal effort.

Secure: An RDBMS offers robust security features, such as encryption , to safeguard your database and the confidential data stored in it. 

Scalable: An RDBMS can scale as your business grows and accommodate more data, users, applications, etc. 

Cost-effective: You can set up and configure the relational database model without any expert assistance or training, thus saving the cost of hiring external consultants.

Isla Sibanda [ 1 ] , an ethical hacker and network security specialist, shares her experience with using an RDBMS.

I and my team appreciate the capabilities of our RDBMS. We use it because it seems perfect for our requirements. We efficiently handle our customers' data, extract useful insights from it, and forecast future client behavior to provide them with more relevant offers or personalized marketing communications. I can say that the RDBMS has played a strong part in snowballing our customer retention rates.

Isla Sibanda

Network security specialist

An object-oriented database management system (OODBMS) stores data in the form of objects and their relationships. It uses object-oriented programming languages such as Python, R, and PHP to access and manage data. This makes it easy for your team to program complex applications that need to work with the data stored in the database. 

This database model is often used to store complex data types and maintain the relationships between them. It’s a good option if you’re seeking high control over data when connecting the DBMS to your other business applications. 

As a small business, you can use the OODBMS data model for web applications because it’s designed to support the complex data structures commonly used in web apps. It also supports concurrency, which enables web apps to handle multiple user requests simultaneously.

Example of the structure of an object-oriented database management system

Why you should invest in an object-oriented database management system

More flexibility and customization: An OODBMS is more flexible and customizable than an RDBMS and can be used to tailor your database as per specific business needs.

Improved performance: To store data in a database, it must first be converted into a compatible format. An OODBMS eliminates this data conversion step by storing and managing data in its native format. This helps save time and improve database performance. 

Less complexity: An OODBMS allows your developers to create objects that represent real-world entities such as name, age group, and address. This helps reduce the complexity of your database.

Better control: An OODBMS offers various data control features, such as access permissions, data backups, and data restoration, to help you maintain oversight of data usage and prevent unauthorized users from modifying the data. 

Increased scalability: An OODBMS is designed to accommodate the growth of your small business. It provides flexible data storage to easily scale up your operations without having to invest in new hardware or infrastructure. 

The hierarchical model organizes data in a tree-like structure, making it easy for users to access and track their data. In a hierarchical database management system (HDBMS), data is organized into a hierarchy, with each level representing a different category of information. 

Let’s say you want to manage the increasing volume of employee information in your database. You can use an HDBMS to create a top-level category for different departments, within which each department will have its own subcategory containing employee data for that department. You can easily access and update information on employees from each department, generate reports, and perform other analyses on the data to better manage and understand your workforce.

Even the U.S. Census Bureau uses the hierarchical data model to track, access, and update data on the population of each state [ 2 ] . 

Example of the structure of a hierarchical database management system

Why you should invest in a hierarchical database management system

Ease of use: An HDBMS organizes data in a tree-like structure, which makes it easy to understand and use for everyone on your team.

Reduced costs: An HDBMS automatically optimizes database storage and data processing, helping your small business save money.

Enhanced decision-making: An HDBMS lets you retrieve and analyze data efficiently and generate reports to help make smarter business decisions. 

Faster performance: An HDBMS offers faster query performance than a flat database (where data is stored only in a single table) by traversing the hierarchy and retrieving the data you need.

Better data security: An HDBMS offers security features such as encryption, data integrity, user authentication, and data audits to protect your data.

A network database management system is designed to store, retrieve, and manage data in a networked environment. It allows sharing of data among multiple users on a network. It not only manages the data within the network but also ensures it’s consistent across all connected devices.

This database model is beneficial if you have a wide area network (WAN) and need to share data across locations. You can also use it when you want to ensure data consistency across the different divisions in your business. A network DBMS is more secure than other types of DBMS because data is spread out on a network, making it much more difficult for hackers to breach.

Example of the structure of a network database management system

Why you should invest in a network database management system

Organized inventory: A network DBMS lets you track inventory levels across products and departments so you know when it’s time to reorder items to avoid stockouts. 

Better employee management : With a network DBMS, you can organize employees’ contact information, job histories, and performance reviews to identify top performers or address issues with underperforming staff.

Financial data management: A network DBMS helps monitor financial data, such as invoices, payments, and receipts, so you can track spending and cash flow.

Improved decision-making: A network DBMS provides access to accurate, up-to-date data, which you can use to gain insights into daily operations, assess marketing campaigns, devise pricing strategies, and make other crucial decisions that influence the growth of your small business.

Get the perfect database management system for your small business

Here are some tips for you to select the right DBMS for your small business needs:

Determine your business's specific needs. Consider factors such as the types and amount of data you need to store, the number of users who need to access the DBMS software, and the types of operations you need to perform on the data.

Consider the cost of implementing and maintaining the database system. Some systems may require a significant upfront investment, while others may have ongoing costs for licensing, support, and updates.

Choose a user-friendly database system. This will help ensure your employees can quickly learn how to use the database management software and that it doesn't become a hindrance to your business operations.

Consider the database system’s long-term scalability. As your business grows, you’ll need a database management system that can adapt and handle increased data storage and processing needs.

Interested in data management? Check out the following resources:

5 Ways to Use Data in the Workplace to Recommend Process Improvements and Drive Efficiency

What Is Master Data Management? Why You Should Care, and What You Need to Know

9 Best Open-Source Database Software

Basics of Database Architecture: A Guide for Small Businesses

Isla Sibanda , LinkedIn

Hierarchy diagrams , United States Census Bureau

Was this article helpful?

About the author s.

Bhavya Aggarwal is a Technical Content Writer at Capterra, covering Information Technology, Cybersecurity, and Emerging Technologies, with a focus on improving IT for small to midsize businesses. He has more than five years of experience in persuasive and fact-based content creation, and his work has been featured in branded publications such as Gartner, Sprinklr, YourStory, etc.

Bhavya has a bachelor’s degree in commerce with a strong background in mass communication and digital marketing. He is a tech geek in the true sense with a passion for staying on top of what’s new in artificial intelligence and emerging technologies for end-consumers. Bhavya lives in India’s capital, Delhi, with his family of four.

Rina is a senior editor at Capterra. She has close to a decade of experience creating and editing content, especially for the IT, software, and finance domains. Passionate about minimalist storytelling, she prioritizes breaking down complex industry jargon into engaging stories accessible to all readers.

Rina holds a postgraduate degree in mass communication and journalism and a bachelor's degree in English literature. She started her career as a features writer for The Times of India, India’s premier English daily newspaper. Outside of work, she’s a doting mother to her dog daughter Puppy, a budding resin artist, and a proponent of financial literacy for women.

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Learn SQL: CREATE DATABASE & CREATE TABLE Operations

Welcome to the first article in the Learn SQL series. In this part, we’ll start with two essential commands in SQL: Create Database and Create Table. While both are pretty simple, they should be used first before you start working on anything with data (unless you use some template database).

Later in this series, I’ll try to cover everything essential for the complete beginner to jump into the magical world of SQL and databases. So, let’s start:

The goal of this article is to create a database (using the SQL Create Database command) and two tables (using the SQL Create Table command) as shown in the picture above. In the upcoming articles, we’ll insert data into these tables, update and delete data, but also add new tables and create queries.

What is a database?

Before we create a database using the SQL Create database command, I want to define what a database is. I’ll use the definition provided by Oracle:

A database is an organized collection of structured information, or data, typically stored electronically in a computer system. A database is usually controlled by a database management system (DBMS). (source: https://www.oracle.com/database/what-is-database.html )

In this article, I’ll use the Microsoft SQL Server Express edition. So, DBMS is SQL Server, and the language we’ll use is T-SQL. Once again I’ll use a quote:

T-SQL (Transact-SQL) is a set of programming extensions from Sybase and Microsoft that add several features to the Structured Query Language (SQL), including transaction control, exception and error handling, row processing and declared variables. (source: https://searchsqlserver.techtarget.com/definition/T-SQL )

I won’t go in-depth in this article, but we can conclude this part with a statement that a database is an organized set of tables that contain data from the real-world and some additional columns needed for the system to work properly. We’ll discuss these in upcoming articles.

SQL Create Database statement

After installing and opening Microsoft SQL Server Management Studio , our screen looks something like this:

It doesn’t look fun at all. We’ll make it more fun by creating a new database. After clicking on the New Query , the new window opens and we’re able to type something in. It looks like on the picture below:

Before typing anything, we should be sure we’re typing it in the right way. T-SQL is a language and as such it has its’ words – set of rules on how to write different commands.

Luckily, one of these commands is the SQL Create Database command. You can see the full T-SQL Create Database syntax on Microsoft pages .

I’ll simplify it a lot and go only with the most basic form. In order to create a new database on our server, we need to use the following command:

Where we’ll use the desired name instead of the database_name .

SQL Create Database example

OK, let’s try it. We’ll run a command:

After running this command, our database is created, and you can see it in the databases list:

Click on the + next to the folder Databases , and besides two folders, you’ll also see that our_first_database had been created.

This is cool and you’ve just successfully created your first database. The problem is that we don’t have anything stored inside the database. Let’s change that.

SQL Create Table statement

In database theory, a table is a structure (“basic unit”) used to store data in the database.

I love to use analogies a lot, so I’ll do it here too. If you think of a library, a database is one shelf with books, and each book is a table. Each book has its own contents but is somehow related to other books on the same shelf – either by sharing some properties, either by just being close.

There is a lot of theory behind database tables, and how to decide what goes where, but the simplest you can do is following. When we look at our data and we need to decide what goes where we should group data in tables in such a manner that everything that belongs to the same real-life entity goes to the same table.

E.g. if we want to store data describing cities and countries, we’ll have two separate tables in our database – one for cities and another one for countries. We won’t mix their data but rather relate them. This goes out of the scope of this article and shall be covered in the upcoming parts of this series.

To define a table, we’ll follow the syntax. You can see full T-SQL Create Table syntax here , but I’ll once more simplify the statement:

We’ll simply choose the name for our table and list all the columns we want to have in this table. Columns are also called attributes and each column describes a property of one record in the table. The column has its type and we should choose the type based on values we expect in that column (number, text, etc.).

SQL Create Table example

Let’s take a look at the definition of our two tables:

First, we’ll define the city table.

Please notice a few things:

• NOT NULL -> This is a property telling us that this column can’t be empty (must be defined)
• IDENTITY(1, 1) -> is also a property of the column telling us that this value shall be generated automatically, starting from 1 and increasing by 1 for each new record
• CONSTRAINT city_pk PRIMARY KEY (id) -> This is not a column, but the rule, telling us that column id shall contain only UNIQUE values. So only 1 city can have id =5

Here we have 1 new CONSTRAINT and that is the UNIQUE constraining. This one tells us that this value must be UNIQUE within this table. E.g. CONSTRAINT country_ak_1 UNIQUE (country_name) defines that we can’t store 2 countries with the same name.

The last part of the script is the definition of foreign keys. We have only 1 such key and it relates city and country table ( city.county_id = country.id ).

Keys (primary and foreign) are too complex and shall be covered in a separate article. After executing these commands, the status of our database is as in the picture below:

Congratulations. You have successfully created your first database using SQL Create Database and Create Table commands. We have 2 tables in our database. Now we’re ready to populate them with data and test if we did it as expected. We’ll do it in the next article, so, stay tuned!

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• Recent Posts

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Chapter 9 Notes for Module 7

What is an information system? What is its purpose?

An information system is a system that

• provides the conditions for data collection, storage, and retrieval
• facilitates the transformation of data into information
• provides management of both data and information.

An information system is composed of hardware, software (DBMS and applications), the database(s), procedures, and people.

Good decisions are generally based on good information. Ultimately, the purpose of an information system is to facilitate good decision making by making relevant and timely information available to the decision makers.

How do systems analysis and systems development fit into a discussion about information systems?

Both systems analysis and systems development constitute part of the Systems Development Life Cycle, or SDLC. Systems analysis, phase II of the SDLC, establishes the need for and the extent of an information system by

• Establishing end-user requirements.
• Evaluating the existing system.
• Developing a logical systems design.

Systems development, based on the detailed systems design found in phase III of the SDLC, yields the information system. The detailed system specifications are established during the systems design phase, in which the designer completes the design of all required system processes.

What does the acronym SDLC mean, and what does an SDLC portray?

SDLC is the acronym that is used to label the System Development Life Cycle. The SDLC traces the history of a information system from its inception to its obsolescence. The SDLC is composed of six phases: planning, analysis, detailed system, design, implementation and maintenance.

What does the acronym DBLC mean, and what does a DBLC portray?

DBLC is the acronym that is used to label the Database Life Cycle. The DBLC traces the history of a database system from its inception to its obsolescence. Since the database constitutes the core of an information system, the DBLC is concurrent to the SDLC. The DBLC is composed of six phases: initial study, design, implementation and loading, testing and evaluation, operation, and maintenance and evolution.

What is the minimal data rule in conceptual design? Why is it important?

The minimal data rule specifies that all the data defined in the data model are actually required to fit present and expected future data requirements. This rule may be phrased as  All that is needed is there, and all that is there is needed .

What are business rules? Why are they important to a database designer?

Business rules are narrative descriptions of the business policies, procedures, or principles that are derived from a detailed description of operations. Business rules are particularly valuable to database designers, because they help define:

• Relationships (1:1, 1:M, M:N, expressed through connectivities and cardinalities)
• Constraints

To develop an accurate data model, the database designer must have a thorough and complete understanding of the organization’s data requirements. The business rules are very important to the designer because they enable the designer to fully understand how the business works and what role is played by data within company operations.

What is the data dictionary’s function in database design?

A good data dictionary provides a precise description of the characteristics of all the entities and attributes found within the database. The data dictionary thus makes it easier to check for the existence of synonyms and homonyms, to check whether all attributes exist to support required reports, to verify appropriate relationship representations, and so on. The data dictionary’s contents are both developed and used during the six DBLC phases:

DATABASE INITIAL STUDY

The basic data dictionary components are developed as the entities and attributes are defined during this phase.

DATABASE DESIGN

The data dictionary contents are used to verify the database design components: entities, attributes, and their relationships. The designer also uses the data dictionary to check the database  design for homonyms and synonyms and verifies that the entities and attributes will support all required query and report requirements.

IMPLEMENTATION AND LOADING

The DBMS’s data dictionary helps to resolve any remaining attribute definition inconsistencies.

TESTING AND EVALUATION

If problems develop during this phase, the data dictionary contents may be used to help restructure the basic design components to make sure that they support all required operations.

If the database design still yields (the almost inevitable) operational glitches, the data dictionary may be used as a quality control device to ensure that operational modifications to the database do not conflict with existing components.

MAINTENANCE AND EVOLUTION

As users face inevitable changes in information needs, the database may be modified to support those needs. Perhaps entities, attributes, and relationships must be added, or relationships must be changed. If new database components are fit into the design, their introduction may produce conflict with existing components. The data dictionary turns out to be a very useful tool to check whether a suggested change invites conflicts within the database design and, if so, how such conflicts may be resolved.

What factors are important in a DBMS software selection?

The selection of DBMS software is critical to the information system’s smooth operation. Consequently, the advantages and disadvantages of the proposed DBMS software should be carefully studied. To avoid false expectations, the end user must be made aware of the limitations of both the DBMS and the database.

Although the factors affecting the purchasing decision vary from company to company, some of the most common are:

• Cost . Purchase, maintenance, operational, license, installation, training, and conversion costs.
• DBMS features and tools . Some database software includes a variety of tools that facilitate the application development task. For example, the availability of query by example (QBE), screen painters, report generators, application generators, data dictionaries, and so on, helps to create a more pleasant work environment for both the end user and the application programmer. Database administrator facilities, query facilities, ease of use, performance, security, concurrency control, transaction processing, and third-party support also influence DBMS software selection.
• Underlying model . Hierarchical, network, relational, object/relational, or object.
• Portability . Across platforms, systems, and languages.
• DBMS hardware requirements . Processor(s), RAM, disk space, and so on.

What three levels of backup may be used in database recovery management? Briefly describe what each of those three backup levels does.

A  full backup  of the database creates a backup copy of all database objects in their entirety.

A  differential backup  of the database creates a backup of only those database objects that have changed since the last full backup.

A  transaction log backup  does not create a backup of database objects, but makes a backup of the log of changes that have been applied to the database objects since the last backup.

Chapter 10 Notes for Module 7

What is meant by the following statement: a transaction is a logical unit of work.

A transaction is a logical unit of work that must be entirely completed of aborted; no intermediate states are accepted. In other words, a transaction, composed of several database requests, is treated by the DBMS as a  unit  of work in which all transaction steps must be fully completed if the transaction is to be accepted by the DBMS.

Acceptance of an incomplete transaction will yield an inconsistent database state. To avoid such a state, the DBMS ensures that all of a transaction’s database operations are completed before they are committed to the database. For example, a credit sale requires a minimum of three database operations:

• An invoice is created for the sold product.
• The product’s inventory quantity on hand is reduced.
• The customer accounts payable balance is increased by the amount listed on the invoice.

If only parts 1 and 2 are completed, the database will be left in an inconsistent state. Unless all three parts (1, 2, and 3) are completed, the entire sales transaction is canceled.

What is a consistent database state, and how is it achieved?

A consistent database state is one in which all data integrity constraints are satisfied. To achieve a consistent database state, a transaction must take the database from one consistent state to another.

The DBMS does not guarantee that the semantic meaning of the transaction truly represents the real-world event. What are the possible consequences of that limitation? Give an example.

The database is designed to verify the syntactic accuracy of the database commands given by the user to be executed by the DBMS. The DBMS will check that the database exists, that the referenced attributes exist in the selected tables, that the attribute data types are correct, and so on. Unfortunately, the DBMS is not designed to guarantee that the syntactically correct transaction accurately represents the real‑world event.

For example, if the end user sells 10 units of product 100179 (Crystal Vases), the DBMS cannot detect errors such as the operator entering 10 units of product 100197 (Crystal Glasses). The DBMS will execute the transaction, and the database will end up in a  technically consistent state  but in a  real‑world inconsistent state  because the wrong product was updated.

What are the five transaction properties and what do they mean?

The five transaction properties are:

Atomicity –  requires that  all  parts of a transaction must be completed or the transaction is aborted. This property ensures that the database will remain in a consistent state.

Consistency –  Indicates the permanence of the database consistent state .

Isolation –  means that the data required by an executing transaction cannot be accessed by any other transaction until the first transaction finishes. This property ensures data consistency for concurrently executing transactions.

Durability –  indicates that the database will be in a  permanent  consistent state after the execution of a transaction. In other words, once a consistent state is reached, it cannot be lost.

Serializability –  means that a series of concurrent transactions will yield the same result as if they were executed one after another.

All five transaction properties work together to make sure that a database maintains data integrity and consistency for either a single-user or a multi-user DBMS.

What does serializability of transactions mean?

Serializability of transactions means that a series of concurrent transactions will yield the same result as if they were executed one after another

What is a transaction log, and what is its function?

The transaction log is a special DBMS table that contains a description of all the database transactions executed by the DBMS. The database transaction log plays a crucial role in maintaining database concurrency control and integrity.

The information stored in the log is used by the DBMS to recover the database after a transaction is aborted or after a system failure. The transaction log is usually stored in a different hard disk or in a different media (tape) to prevent the failure caused by a media error.

What is a scheduler, what does it do, and why is its activity important to concurrency control?

The scheduler is the DBMS component that establishes the order in which concurrent database operations are executed. The scheduler interleaves the execution of the database operations (belonging to several concurrent transactions) to ensure the  serializability  of transactions. In other words, the scheduler guarantees that the execution of concurrent transactions will yield the same result as though the transactions were executed one after another. The scheduler is important because it is the DBMS component that will ensure transaction serializability. In other words, the scheduler allows the concurrent execution of transactions, giving end users the impression that they are the DBMS’s only users.

What is a lock, and how, in general, does it work?

A lock is a mechanism used in concurrency control to guarantee the exclusive use of a data element to the transaction that owns the lock. For example, if the data element X is currently locked by transaction T1, transaction T2 will not have access to the data element X until T1 releases its lock.

Generally speaking, a data item can be in only two states: locked (being used by some transaction) or unlocked (not in use by any transaction). To access a data element X, a transaction T1 first must request a lock to the DBMS. If the data element is not in use, the DBMS will lock X to be used by T1 exclusively. No other transaction will have access to X while T1 is executed.

What are the different levels of lock granularity?

Lock granularity  refers to the size of the database object that a single lock is placed upon.  Lock granularity can be:

•        Database-level, meaning the entire database is locked by one lock.
•       Table-level, meaning a table is locked by one lock.
•       Page-level, meaning a diskpage is locked by one lock.
•       Row-level, meaning one row is locked by one lock.
•       Field-level, meaning one field in one row is locked by one lock.

Why might a page-level lock be preferred over a field-level lock?

Smaller lock granualarity improves the concurrency of the database by reducing contention to lock database objects.  However, smaller lock granularity also means that more locks must be maintained and managed by the DBMS, requiring more processing overhead and system resources for lock management.  Concurrency demands and system resource usage must be balanced to ensure the best overall transaction performance.  In some circumstances, page-level locks, which require fewer system resources, may produce better overall performance than field-level locks, which require more system resources.

What is concurrency control, and what is its objective?

Concurrency control is the activity of coordinating the simultaneous execution of transactions in a multiprocessing or multi‑user database management system. The objective of concurrency control is to ensure the serializability of transactions in a multi‑user database management system. (The DBMS’s scheduler is in charge of maintaining concurrency control.)

Because it helps to guarantee data integrity and consistency in a database system, concurrency control is one of the most critical activities performed by a DBMS. If concurrency control is not maintained, three serious problems may be caused by concurrent transaction execution: lost updates, uncommitted data, and inconsistent retrievals.

What is an exclusive lock, and under what circumstances is it granted?

An exclusive lock is one of two lock types used to enforce concurrency control.  (A lock can have three states: unlocked, shared (read) lock, and exclusive (write) lock. The “shared” and “exclusive” labels indicate the nature of the lock.)

An exclusive lock exists when access to a data item is specifically reserved for the transaction that locked the object. The exclusive lock must be used when a potential for conflict exists, e.g., when one or more transactions must update (WRITE) a data item. Therefore, an exclusive lock is issued only when a transaction must WRITE (update) a data item  and no locks are currently held on that data item by any other transaction .

To understand the reasons for having an exclusive lock, look at its counterpart, the shared lock. Shared locks are appropriate when concurrent transactions are granted READ access on the basis of a common lock, because concurrent transactions based on a READ cannot produce a conflict.

A shared lock is issued when a transaction must read data from the database  and no exclusive locks are held  on the data to be read.

What is a deadlock, and how can it be avoided? What are some strategies for dealing with deadlocks?

Base your discussion on Chapter 10’s Section 10.3.4, Deadlocks. Start by pointing out that, although locks prevent serious data inconsistencies, their use may lead to two major problems:

• The transaction schedule dictated by the locking requirements may not be serializable, thus causing data integrity and consistency problems.
• The schedule may create  deadlocks . Database deadlocks are the equivalent of a traffic gridlock in a big city and are caused by two transactions waiting for each other to unlock data.

Refer to Table 10.13 on page 473 of the text.

In a real world DBMS, many more transactions can be executed simultaneously, thereby increasing the probability of generating deadlocks. Note that deadlocks are possible only if one of the transactions wants to obtain an exclusive lock on a data item; no deadlock condition can exist among shared locks.

Three basic techniques exist to control deadlocks:

Deadlock Prevention

A transaction requesting a new lock is aborted if there is a possibility that a deadlock may occur. If the transaction is aborted, all the changes made by this transaction are rolled back and all locks are released. The transaction is then re-scheduled for execution. Deadlock prevention works because it avoids the conditions that lead to deadlocking.

Deadlock Detection

The DBMS periodically tests the database for deadlocks. If a deadlock is found, one of the transactions (the “victim”) is aborted (rolled back and rescheduled) and the other transaction continues. Note particularly the discussion in Section 10.4.1, Wait/Die and Wound/Wait Schemes.

Deadlock Avoidance

The transaction must obtain all the locks it needs before it can be executed. This technique avoids rollback of conflicting transactions by requiring that locks be obtained in succession. However, the serial lock assignment required in deadlock avoidance increases the response times.

The best deadlock control method depends on the database environment. For example, if the probability of deadlocks is low, deadlock detection is recommended. However, if the probability of deadlocks is high, deadlock prevention is recommended. If response time is not high on the system priority list, deadlock avoidance may be employed.

What are some disadvantages of time-stamping methods for concurrency control?

The disadvantages are: 1) each value stored in the database requires two additional time stamp fields – one for the last time the field was read and one for the last time it was updated, 2) increased memory and processing overhead requirements, and 3) many transactions may have to be stopped, rescheduled, and restamped.

Why might it take a long time to complete transactions when an optimistic approach to concurrency control is used?

Because the optimistic approach makes the assumption that conflict from concurrent transactions is unlikely, it does nothing to avoid conflicts or control the conflicts.  The only test for conflict occurs during the validation phase.  If a conflict is detected, then the entire transaction restarts.  In an environment with few conflicts from concurrency, this type of single checking scheme works well.  In an environment where conflicts are common, a transaction may have to be restarted numerous times before it can be written to the database.

What are the three types of database critical events that can trigger the database recovery process? Give some examples for each one.

Backup and recovery functions constitute a very important component of today’s DBMSs. Some DBMSs provide functions that allow the database administrator to perform and schedule automatic database backups to permanent secondary storage devices, such as disks or tapes.

Critical events include:

• Hardware/software failures. hard disk media failure, a bad capacitor on a motherboard, or a failing memory bank. Other causes of errors under this category include application program or operating system errors that cause data to be overwritten, deleted, or lost.
• Human-caused incidents. This type of event can be categorized as unintentional or intentional.

An unintentional failure is caused by carelessness by end-users. Such errors include deleting the wrong rows from a table, pressing the wrong key on the keyboard, or shutting down the main database server by accident.

Intentional events are of a more severe nature and normally indicate that the company data are at serious risk. Under this category are security threats caused by hackers trying to gain unauthorized access to data resources and virus attacks caused by disgruntled employees trying to compromise the database operation and damage the company.

• Natural disasters. This category includes fires, earthquakes, floods, and power failures.

What are the four ANSI transaction isolation levels? What type of reads does each level allow?

The four ANSI transaction isolation levels are 1) read uncommitted, 2) read committed, 3) repeatable read, and 4) Serializable.  These levels allow different “questionable” reads.  A read is questionable if it can produce inconsistent results.  Read uncommitted isolation will allow dirty reads, non-repeatable reads and phantom reads.  Read committed isolation will allow non-repeatable reads and phantom reads.  Repeatable read isolation will allow phantom reads.  Serializable does not allow any questionable reads

Database Management Systems Copyright © by Ronald Danault is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Data Model Types: An Explanation with Examples

Martyna is a software developer with a passion for programming, automation, and innovation. Currently working for the global corporate IT consulting sector, she is a new-generation techie who hails from Warsaw University of Technology, Poland. She has certifications in both database administration and Java development. Her interests include creating small automation devices using embedded systems and various electronic components. In her free time, she practices yoga, which helps her to center her thoughts and come up with new ideas.

Data modeling is an essential part of designing a database. If you want to achieve the best outcome, make sure to utilize the available data models. Read on to find out more.

Every relational database has clearly defined objects and relationships among these objects. Together, they comprise the data model.

This article presents the concept of data modeling. First, we’ll go over data modeling and the steps of its process. Then we’ll jump into the various types of data models. You’ll see examples of conceptual, logical, and physical data models. I’ll also mention some of the more specific data models.

Let’s get started!

About Data Modeling

Relational databases organize data into tables that have connections among them. But before creating a physical database, you should model your data. Data models help visualize data and group it logically. Below are the three data models we’ll be focusing on in this article:

The base is the conceptual data model. What follows are the logical and physical data models. We’ll find out more about these data models in the following sections.

Data modeling is a vast subject. It is essential to the database design process. So make sure to check out our other articles on data modeling, such as What is Data Modeling , The Benefits of Data Modeling , and Why Do You Need Data Modeling . And if you still ask why we need data modeling processes and diagrams, read this article to learn about common database design errors that could be avoided by following the data modeling process.

The Data Modeling Process

There are several steps to be followed during the data modeling process. Let’s go through them one by one.

Step 1. Identifying entities

This step is a part of conceptual data modeling. Here, we decide on data groups according to the business rules. For example, when visualizing a grocery shop database, we would have entities such as Products , Orders , and Customers , as shown below:

Step 2. Identifying connections between entities

This step is part of conceptual data modeling. Here, we decide on the relationships (i.e. connections) between entities. For example, each customer would have one or more orders, and each order would have one or more products. We can see this in the image below.

Step 3. Identifying entities’ attributes

This step is part of logical data modeling. Each entity is assigned its attributes; this becomes the base for the physical data model. For example, each order would have an order ID, a customer who placed the order ( customer_id ), and products ordered:

Step 4. Deciding attributes’ specific data types

This step is part of physical data modeling. Here, we assign database-specific data types to the attributes of each entity. For example, an order_id would be an INTEGER and a customer name would be VARCHAR, as shown below.

Step 5. Identifying many-to-many relationships and implementing junction tables

This step is also part of the physical data modeling. Here, we create an additional table that stores many-to-many relationship data. For example, each order can have one or more products, and at the same time, each product can be ordered zero or more times.

Step 6. Creating database constraints, indices, triggers, and other database-specific objects

This step is part of physical data modeling. Here, we focus on implementing database-specific features. For example, let’s mark the primary keys and foreign keys (if needed) for each table:

Vertabelo lets you create an SQL script from the physical data model; when you complete the data modeling process, you can create your physical database in no time by executing the Vertabelo-provided SQL script.

Data modeling is part of database modeling. Check out this article to get a different perspective on the database modeling process as a whole.

Common Data Models

You now know the basics of the data modeling process. Let’s see how you might use it in practice.

Imagine that the local zoo hired you to design their database. We’ll create conceptual, logical, and physical data models to complete the entire database design process.

Conceptual Data Model

The conceptual data model focuses on identifying entities and relationships among them. We take into consideration business data, business rules, and business processes.

This data model is a strictly abstract representation of data. Its components include:

• Entities representing groups of objects that share attributes (which are defined later, in the logical model).
• Relationships between

Conceptual data models are typically created by data architects to present a high-level data overview to business stakeholders.

First, let’s identify the entities.

• Zoo_Employee stores data about the employees of the zoo.
• Zoo_Animal stores data about the animals living in the zoo.
• Animal_Species stores data on the animal species present in the zoo.
• Animal_Food_Type stores the types of food eaten by the zoo’s animals.
• Food_Provider stores data about companies or organizations that provide food types.

Now let’s discuss the relationships among the entities.

• Each animal has one caretaker, who is an employee of the zoo.
• Each employee can be a caretaker of zero or more
• Each animal has a species and eats a specific food type.
• Each food type is provided by one or more food providers, and each food provider can provide one or more food types.

This is the conceptual model to represent this data:

Next, let’s move on to the logical data model.

Logical Data Model

A logical data model dives deep into the data structure, assigns attributes to each entity, and specifies the database implementation details.

This data model is a base for the physical data model. The only difference is that logical data models are not database-specific (as opposed to physical data models, which are designed for one database management system like Oracle or MySQL).

We can create the logical data model in Vertabelo. Notice how many more details there are:

In addition to the attribute names, we have general data types (i.e. integer or varchar) and indicators for mandatory or non-nullable columns (M) and primary identifier fields (PI). PI fields will become primary keys in the physical data model.

This data model is still database-agnostic. The attributes’ data types are abstract, but Vertabelo converts them into database-specific data types when generating a physical data model.

Physical Data Model

The physical data model includes all database-specific features, such as data types, database indices, triggers, constraints, and more.

This data model is directly related to the database, as we can generate the database creation script solely based on this data model. It includes primary and foreign keys, column and value constraints, and other database-specific features.

Let’s generate a physical data model from our logical data model in Vertabelo.

This data model is database-specific. Here, we’re using PostgreSQL. To learn even more about the conceptual, logical, and physical data models, read this article .

Now that we’ve learned about the fundamental data models, let’s look at other available data models.

Other Data Model Examples

There are many different data models. The Unified Modeling Language (UML) offers various models used in software engineering. Some of them, such as a class diagram, are helpful in data modeling. Let’s look at some other useful data models.

Dimensional Data Model

Dimensional data models are used to implement data warehousing systems. These data models are handy in facilitating the analysis and retrieval of data.

The elements of a dimensional data model include:

• Facts, i.e. business processes whose information can be retrieved.
• Dimension, e. details for each fact. These usually answer the questions of who, where, and what.

For example, if we consider the feeding of an animal business process to be a fact, then the possible dimensions include the caretaker dimension, food type dimension, and feeding time dimension.

Object-Oriented Data Model

An object-oriented data model helps us more easily relate complex real-world objects. The elements of this model include:

• Class, e. a template for object creation.
• Object, e. an instance of a class.
• Attributes that characterize each object.
• Methods that describe the behavior of objects.

Below we have a  sample object-oriented data model:

This data model provides more information on the specificities of each object/entity,

Entity-Relationship Data Model

The entity-relationship data model falls under the category of conceptual data models. It consists of entities, their attributes, and any relationships among entities.

Conceptual data models are all about the correct perception of data.

Try Your Hand at Different Data Model Types!

Any database design process begins with visualizing the data using various data modeling tools and diagrams. We usually use a top-down approach, starting with a general overview of the available data (conceptual models) and then drilling down to more and more details (logical and physical models).

Following this approach, the first step is to create a conceptual data model. It helps us initially organize the data and decide on the objects/entities and relationships among them. Next comes a logical data model that provides more details on the data structure, such as the attributes of each entity. At last, we convert a logical data model to a physical data model. A physical data model is an exact blueprint of your database.

With that knowledge, you’re ready to design your own database.

You may also like

What is data modeling, the benefits of data modeling, why do you need data modeling, the 9 most common database design errors, what i like about database modeling, what are conceptual, logical, and physical data models.

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Not all data are created equal; some are structured, but most of them are unstructured. Structured and unstructured data are sourced, collected and scaled in different ways and each one resides in a different type of database.

In this article, we will take a deep dive into both types so that you can get the most out of your data.

Structured data—typically categorized as quantitative data—is highly organized and easily decipherable by  machine learning algorithms .  Developed by IBM® in 1974 , structured query language (SQL) is the programming language used to manage structured data. By using a  relational (SQL) database , business users can quickly input, search and manipulate structured data.

Examples of structured data include dates, names, addresses, credit card numbers, among others. Their benefits are tied to ease of use and access, while liabilities revolve around data inflexibility:

• Easily used by machine learning (ML) algorithms:  The specific and organized architecture of structured data eases the manipulation and querying of ML data.
• Easily used by business users:  Structured data do not require an in-depth understanding of different types of data and how they function. With a basic understanding of the topic relative to the data, users can easily access and interpret the data.
• Accessible by more tools:  Since structured data predates unstructured data, there are more tools available for using and analyzing structured data.
• Limited usage:  Data with a predefined structure can only be used for its intended purpose, which limits its flexibility and usability.
• Limited storage options:  Structured data are usually stored in data storage systems with rigid schemas (for example, “ data warehouses ”). Therefore, changes in data requirements necessitate an update of all structured data, which leads to a massive expenditure of time and resources.
• OLAP :  Performs high-speed, multidimensional data analysis from unified, centralized data stores.
• SQLite : (link resides outside ibm.com)  Implements a self-contained,  serverless , zero-configuration, transactional relational database engine.
• MySQL :  Embeds data into mass-deployed software, particularly mission-critical, heavy-load production system.
• PostgreSQL :  Supports SQL and JSON querying as well as high-tier programming languages (C/C+, Java,  Python , among others.).
• Customer relationship management (CRM):  CRM software runs structured data through analytical tools to create datasets that reveal customer behavior patterns and trends.
• Online booking:  Hotel and ticket reservation data (for example, dates, prices, destinations, among others.) fits the “rows and columns” format indicative of the pre-defined data model.
• Accounting:  Accounting firms or departments use structured data to process and record financial transactions.

Unstructured data, typically categorized as qualitative data, cannot be processed and analyzed through conventional data tools and methods. Since unstructured data does not have a predefined data model, it is best managed in  non-relational (NoSQL) databases . Another way to manage unstructured data is to use  data lakes  to preserve it in raw form.

The importance of unstructured data is rapidly increasing.  Recent projections  (link resides outside ibm.com) indicate that unstructured data is over 80% of all enterprise data, while 95% of businesses prioritize unstructured data management.

Examples of unstructured data include text, mobile activity, social media posts, Internet of Things (IoT) sensor data, among others. Their benefits involve advantages in format, speed and storage, while liabilities revolve around expertise and available resources:

• Native format:  Unstructured data, stored in its native format, remains undefined until needed. Its adaptability increases file formats in the database, which widens the data pool and enables data scientists to prepare and analyze only the data they need.
• Fast accumulation rates:  Since there is no need to predefine the data, it can be collected quickly and easily.
• Data lake storage:  Allows for massive storage and pay-as-you-use pricing, which cuts costs and eases scalability.
• Requires expertise:  Due to its undefined or non-formatted nature, data science expertise is required to prepare and analyze unstructured data. This is beneficial to data analysts but alienates unspecialized business users who might not fully understand specialized data topics or how to utilize their data.
• Specialized tools:  Specialized tools are required to manipulate unstructured data, which limits product choices for data managers.
• MongoDB :  Uses flexible documents to process data for cross-platform applications and services.
• DynamoDB :  (link resides outside ibm.com) Delivers single-digit millisecond performance at any scale through built-in security, in-memory caching and backup and restore.
• Hadoop :  Provides distributed processing of large data sets using simple programming models and no formatting requirements.
• Azure :  Enables agile cloud computing for creating and managing apps through Microsoft’s data centers.
• Data mining :  Enables businesses to use unstructured data to identify consumer behavior, product sentiment and purchasing patterns to better accommodate their customer base.
• Predictive data analytics :  Alert businesses of important activity ahead of time so they can properly plan and accordingly adjust to significant market shifts.
• Chatbots :  Perform text analysis to route customer questions to the appropriate answer sources.

While structured (quantitative) data gives a “birds-eye view” of customers, unstructured (qualitative) data provides a deeper understanding of customer behavior and intent. Let’s explore some of the key areas of difference and their implications:

• Sources:  Structured data is sourced from GPS sensors, online forms, network logs, web server logs,  OLTP systems , among others; whereas unstructured data sources include email messages, word-processing documents, PDF files, and others.
• Forms:  Structured data consists of numbers and values, whereas unstructured data consists of sensors, text files, audio and video files, among others.
• Models:  Structured data has a predefined data model and is formatted to a set data structure before being placed in data storage (for example, schema-on-write), whereas unstructured data is stored in its native format and not processed until it is used (for example, schema-on-read).
• Storage:  Structured data is stored in tabular formats (for example, excel sheets or SQL databases) that require less storage space. It can be stored in data warehouses, which makes it highly scalable. Unstructured data, on the other hand, is stored as media files or NoSQL databases, which require more space. It can be stored in data lakes, which makes it difficult to scale.
• Uses:  Structured data is used in machine learning (ML) and drives its algorithms, whereas unstructured data is used in  natural language processing  (NLP) and text mining.

Semi-structured data (for example, JSON, CSV, XML) is the “bridge” between structured and unstructured data. It does not have a predefined data model and is more complex than structured data, yet easier to store than unstructured data.

Semi-structured data uses “metadata” (for example, tags and semantic markers) to identify specific data characteristics and scale data into records and preset fields. Metadata ultimately enables semi-structured data to be better cataloged, searched and analyzed than unstructured data.

• Example of metadata usage:  An online article displays a headline, a snippet, a featured image, image alt-text, slug, among others, which helps differentiate one piece of web content from similar pieces.
• Example of semi-structured data vs. structured data:  A tab-delimited file containing customer data versus a database containing CRM tables.
• Example of semi-structured data vs. unstructured data:  A tab-delimited file versus a list of comments from a customer’s Instagram.

Recent developments in  artificial intelligence  (AI) and machine learning (ML) are driving the future wave of data, which is enhancing business intelligence and advancing industrial innovation. In particular, the data formats and models that are covered in this article are helping business users to do the following:

• Analyze digital communications for compliance:  Pattern recognition and email threading analysis software that can search email and chat data for potential noncompliance.
• Track high-volume customer conversations in social media:  Text analytics and sentiment analysis that enables monitoring of marketing campaign results and identifying online threats.
• Gain new marketing intelligence:  ML analytics tools that can quickly cover massive amounts of data to help businesses analyze customer behavior.

Furthermore, smart and efficient usage of data formats and models can help you with the following:

• Understand customer needs at a deeper level to better serve them
• Create more focused and targeted marketing campaigns
• Track current metrics and create new ones
• Create better product opportunities and offerings
• Reduce operational costs

Whether you are a seasoned data expert or a novice business owner, being able to handle all forms of data is conducive to your success. By using structured, semi-structured and unstructured data options, you can perform optimal data management that will ultimately benefit your mission.

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Home » Assignment – Types, Examples and Writing Guide

Assignment – Types, Examples and Writing Guide

Table of Contents

Definition:

Assignment is a task given to students by a teacher or professor, usually as a means of assessing their understanding and application of course material. Assignments can take various forms, including essays, research papers, presentations, problem sets, lab reports, and more.

Assignments are typically designed to be completed outside of class time and may require independent research, critical thinking, and analysis. They are often graded and used as a significant component of a student’s overall course grade. The instructions for an assignment usually specify the goals, requirements, and deadlines for completion, and students are expected to meet these criteria to earn a good grade.

History of Assignment

The use of assignments as a tool for teaching and learning has been a part of education for centuries. Following is a brief history of the Assignment.

• Ancient Times: Assignments such as writing exercises, recitations, and memorization tasks were used to reinforce learning.
• Medieval Period : Universities began to develop the concept of the assignment, with students completing essays, commentaries, and translations to demonstrate their knowledge and understanding of the subject matter.
• 19th Century : With the growth of schools and universities, assignments became more widespread and were used to assess student progress and achievement.
• 20th Century: The rise of distance education and online learning led to the further development of assignments as an integral part of the educational process.
• Present Day: Assignments continue to be used in a variety of educational settings and are seen as an effective way to promote student learning and assess student achievement. The nature and format of assignments continue to evolve in response to changing educational needs and technological innovations.

Types of Assignment

Here are some of the most common types of assignments:

An essay is a piece of writing that presents an argument, analysis, or interpretation of a topic or question. It usually consists of an introduction, body paragraphs, and a conclusion.

Essay structure:

• Introduction : introduces the topic and thesis statement
• Body paragraphs : each paragraph presents a different argument or idea, with evidence and analysis to support it
• Conclusion : summarizes the key points and reiterates the thesis statement

Research paper

A research paper involves gathering and analyzing information on a particular topic, and presenting the findings in a well-structured, documented paper. It usually involves conducting original research, collecting data, and presenting it in a clear, organized manner.

Research paper structure:

• Title page : includes the title of the paper, author’s name, date, and institution
• Abstract : summarizes the paper’s main points and conclusions
• Introduction : provides background information on the topic and research question
• Literature review: summarizes previous research on the topic
• Methodology : explains how the research was conducted
• Results : presents the findings of the research
• Discussion : interprets the results and draws conclusions
• Conclusion : summarizes the key findings and implications

A case study involves analyzing a real-life situation, problem or issue, and presenting a solution or recommendations based on the analysis. It often involves extensive research, data analysis, and critical thinking.

Case study structure:

• Introduction : introduces the case study and its purpose
• Background : provides context and background information on the case
• Analysis : examines the key issues and problems in the case
• Solution/recommendations: proposes solutions or recommendations based on the analysis
• Conclusion: Summarize the key points and implications

A lab report is a scientific document that summarizes the results of a laboratory experiment or research project. It typically includes an introduction, methodology, results, discussion, and conclusion.

Lab report structure:

• Title page : includes the title of the experiment, author’s name, date, and institution
• Abstract : summarizes the purpose, methodology, and results of the experiment
• Methods : explains how the experiment was conducted
• Results : presents the findings of the experiment

Presentation

A presentation involves delivering information, data or findings to an audience, often with the use of visual aids such as slides, charts, or diagrams. It requires clear communication skills, good organization, and effective use of technology.

Presentation structure:

• Introduction : introduces the topic and purpose of the presentation
• Body : presents the main points, findings, or data, with the help of visual aids
• Conclusion : summarizes the key points and provides a closing statement

Creative Project

A creative project is an assignment that requires students to produce something original, such as a painting, sculpture, video, or creative writing piece. It allows students to demonstrate their creativity and artistic skills.

Creative project structure:

• Introduction : introduces the project and its purpose
• Body : presents the creative work, with explanations or descriptions as needed
• Conclusion : summarizes the key elements and reflects on the creative process.

Examples of Assignments

Following are Examples of Assignment templates samples:

Essay template:

I. Introduction

• Hook: Grab the reader’s attention with a catchy opening sentence.
• Background: Provide some context or background information on the topic.
• Thesis statement: State the main argument or point of your essay.

II. Body paragraphs

• Topic sentence: Introduce the main idea or argument of the paragraph.
• Evidence: Provide evidence or examples to support your point.
• Analysis: Explain how the evidence supports your argument.
• Transition: Use a transition sentence to lead into the next paragraph.

III. Conclusion

• Restate thesis: Summarize your main argument or point.
• Review key points: Summarize the main points you made in your essay.
• Concluding thoughts: End with a final thought or call to action.

Research paper template:

I. Title page

• Title: Give your paper a descriptive title.
• Author: Include your name and institutional affiliation.
• Date: Provide the date the paper was submitted.

II. Abstract

• Background: Summarize the background and purpose of your research.
• Methodology: Describe the methods you used to conduct your research.
• Results: Summarize the main findings of your research.
• Conclusion: Provide a brief summary of the implications and conclusions of your research.

III. Introduction

• Background: Provide some background information on the topic.
• Research question: State your research question or hypothesis.
• Purpose: Explain the purpose of your research.

IV. Literature review

• Background: Summarize previous research on the topic.
• Gaps in research: Identify gaps or areas that need further research.

V. Methodology

• Participants: Describe the participants in your study.
• Procedure: Explain the procedure you used to conduct your research.
• Measures: Describe the measures you used to collect data.

VI. Results

• Quantitative results: Summarize the quantitative data you collected.
• Qualitative results: Summarize the qualitative data you collected.

VII. Discussion

• Interpretation: Interpret the results and explain what they mean.
• Implications: Discuss the implications of your research.
• Limitations: Identify any limitations or weaknesses of your research.

VIII. Conclusion

• Review key points: Summarize the main points you made in your paper.

Case study template:

• Background: Provide background information on the case.
• Research question: State the research question or problem you are examining.
• Purpose: Explain the purpose of the case study.

II. Analysis

• Problem: Identify the main problem or issue in the case.
• Factors: Describe the factors that contributed to the problem.
• Alternative solutions: Describe potential solutions to the problem.

III. Solution/recommendations

• Proposed solution: Describe the solution you are proposing.
• Rationale: Explain why this solution is the best one.
• Implementation: Describe how the solution can be implemented.

IV. Conclusion

• Summary: Summarize the main points of your case study.

Lab report template:

• Title: Give your report a descriptive title.
• Date: Provide the date the report was submitted.
• Background: Summarize the background and purpose of the experiment.
• Methodology: Describe the methods you used to conduct the experiment.
• Results: Summarize the main findings of the experiment.
• Conclusion: Provide a brief summary of the implications and conclusions
• Background: Provide some background information on the experiment.
• Hypothesis: State your hypothesis or research question.
• Purpose: Explain the purpose of the experiment.

IV. Materials and methods

• Materials: List the materials and equipment used in the experiment.
• Procedure: Describe the procedure you followed to conduct the experiment.
• Data: Present the data you collected in tables or graphs.
• Analysis: Analyze the data and describe the patterns or trends you observed.

VI. Discussion

• Implications: Discuss the implications of your findings.
• Limitations: Identify any limitations or weaknesses of the experiment.

VII. Conclusion

• Restate hypothesis: Summarize your hypothesis or research question.
• Review key points: Summarize the main points you made in your report.

Presentation template:

• Attention grabber: Grab the audience’s attention with a catchy opening.
• Purpose: Explain the purpose of your presentation.
• Overview: Provide an overview of what you will cover in your presentation.

II. Main points

• Main point 1: Present the first main point of your presentation.
• Supporting details: Provide supporting details or evidence to support your point.
• Main point 2: Present the second main point of your presentation.
• Main point 3: Present the third main point of your presentation.
• Summary: Summarize the main points of your presentation.
• Call to action: End with a final thought or call to action.

Creative writing template:

• Setting: Describe the setting of your story.
• Characters: Introduce the main characters of your story.
• Rising action: Introduce the conflict or problem in your story.
• Climax: Present the most intense moment of the story.
• Falling action: Resolve the conflict or problem in your story.
• Resolution: Describe how the conflict or problem was resolved.
• Final thoughts: End with a final thought or reflection on the story.

How to Write Assignment

Here is a general guide on how to write an assignment:

• Understand the assignment prompt: Before you begin writing, make sure you understand what the assignment requires. Read the prompt carefully and make note of any specific requirements or guidelines.
• Research and gather information: Depending on the type of assignment, you may need to do research to gather information to support your argument or points. Use credible sources such as academic journals, books, and reputable websites.
• Organize your ideas : Once you have gathered all the necessary information, organize your ideas into a clear and logical structure. Consider creating an outline or diagram to help you visualize your ideas.
• Write a draft: Begin writing your assignment using your organized ideas and research. Don’t worry too much about grammar or sentence structure at this point; the goal is to get your thoughts down on paper.
• Revise and edit: After you have written a draft, revise and edit your work. Make sure your ideas are presented in a clear and concise manner, and that your sentences and paragraphs flow smoothly.
• Proofread: Finally, proofread your work for spelling, grammar, and punctuation errors. It’s a good idea to have someone else read over your assignment as well to catch any mistakes you may have missed.
• Submit your assignment : Once you are satisfied with your work, submit your assignment according to the instructions provided by your instructor or professor.

Applications of Assignment

Assignments have many applications across different fields and industries. Here are a few examples:

• Education : Assignments are a common tool used in education to help students learn and demonstrate their knowledge. They can be used to assess a student’s understanding of a particular topic, to develop critical thinking skills, and to improve writing and research abilities.
• Business : Assignments can be used in the business world to assess employee skills, to evaluate job performance, and to provide training opportunities. They can also be used to develop business plans, marketing strategies, and financial projections.
• Journalism : Assignments are often used in journalism to produce news articles, features, and investigative reports. Journalists may be assigned to cover a particular event or topic, or to research and write a story on a specific subject.
• Research : Assignments can be used in research to collect and analyze data, to conduct experiments, and to present findings in written or oral form. Researchers may be assigned to conduct research on a specific topic, to write a research paper, or to present their findings at a conference or seminar.
• Government : Assignments can be used in government to develop policy proposals, to conduct research, and to analyze data. Government officials may be assigned to work on a specific project or to conduct research on a particular topic.
• Non-profit organizations: Assignments can be used in non-profit organizations to develop fundraising strategies, to plan events, and to conduct research. Volunteers may be assigned to work on a specific project or to help with a particular task.

Purpose of Assignment

The purpose of an assignment varies depending on the context in which it is given. However, some common purposes of assignments include:

• Assessing learning: Assignments are often used to assess a student’s understanding of a particular topic or concept. This allows educators to determine if a student has mastered the material or if they need additional support.
• Developing skills: Assignments can be used to develop a wide range of skills, such as critical thinking, problem-solving, research, and communication. Assignments that require students to analyze and synthesize information can help to build these skills.
• Encouraging creativity: Assignments can be designed to encourage students to be creative and think outside the box. This can help to foster innovation and original thinking.
• Providing feedback : Assignments provide an opportunity for teachers to provide feedback to students on their progress and performance. Feedback can help students to understand where they need to improve and to develop a growth mindset.
• Meeting learning objectives : Assignments can be designed to help students meet specific learning objectives or outcomes. For example, a writing assignment may be designed to help students improve their writing skills, while a research assignment may be designed to help students develop their research skills.

When to write Assignment

Assignments are typically given by instructors or professors as part of a course or academic program. The timing of when to write an assignment will depend on the specific requirements of the course or program, but in general, assignments should be completed within the timeframe specified by the instructor or program guidelines.

It is important to begin working on assignments as soon as possible to ensure enough time for research, writing, and revisions. Waiting until the last minute can result in rushed work and lower quality output.

It is also important to prioritize assignments based on their due dates and the amount of work required. This will help to manage time effectively and ensure that all assignments are completed on time.

In addition to assignments given by instructors or professors, there may be other situations where writing an assignment is necessary. For example, in the workplace, assignments may be given to complete a specific project or task. In these situations, it is important to establish clear deadlines and expectations to ensure that the assignment is completed on time and to a high standard.

Characteristics of Assignment

Here are some common characteristics of assignments:

• Purpose : Assignments have a specific purpose, such as assessing knowledge or developing skills. They are designed to help students learn and achieve specific learning objectives.
• Requirements: Assignments have specific requirements that must be met, such as a word count, format, or specific content. These requirements are usually provided by the instructor or professor.
• Deadline: Assignments have a specific deadline for completion, which is usually set by the instructor or professor. It is important to meet the deadline to avoid penalties or lower grades.
• Individual or group work: Assignments can be completed individually or as part of a group. Group assignments may require collaboration and communication with other group members.
• Feedback : Assignments provide an opportunity for feedback from the instructor or professor. This feedback can help students to identify areas of improvement and to develop their skills.
• Academic integrity: Assignments require academic integrity, which means that students must submit original work and avoid plagiarism. This includes citing sources properly and following ethical guidelines.
• Learning outcomes : Assignments are designed to help students achieve specific learning outcomes. These outcomes are usually related to the course objectives and may include developing critical thinking skills, writing abilities, or subject-specific knowledge.

Advantages of Assignment

There are several advantages of assignment, including:

• Helps in learning: Assignments help students to reinforce their learning and understanding of a particular topic. By completing assignments, students get to apply the concepts learned in class, which helps them to better understand and retain the information.
• Develops critical thinking skills: Assignments often require students to think critically and analyze information in order to come up with a solution or answer. This helps to develop their critical thinking skills, which are important for success in many areas of life.
• Encourages creativity: Assignments that require students to create something, such as a piece of writing or a project, can encourage creativity and innovation. This can help students to develop new ideas and perspectives, which can be beneficial in many areas of life.
• Builds time-management skills: Assignments often come with deadlines, which can help students to develop time-management skills. Learning how to manage time effectively is an important skill that can help students to succeed in many areas of life.
• Provides feedback: Assignments provide an opportunity for students to receive feedback on their work. This feedback can help students to identify areas where they need to improve and can help them to grow and develop.

Limitations of Assignment

There are also some limitations of assignments that should be considered, including:

• Limited scope: Assignments are often limited in scope, and may not provide a comprehensive understanding of a particular topic. They may only cover a specific aspect of a topic, and may not provide a full picture of the subject matter.
• Lack of engagement: Some assignments may not engage students in the learning process, particularly if they are repetitive or not challenging enough. This can lead to a lack of motivation and interest in the subject matter.
• Time-consuming: Assignments can be time-consuming, particularly if they require a lot of research or writing. This can be a disadvantage for students who have other commitments, such as work or extracurricular activities.
• Unreliable assessment: The assessment of assignments can be subjective and may not always accurately reflect a student’s understanding or abilities. The grading may be influenced by factors such as the instructor’s personal biases or the student’s writing style.
• Lack of feedback : Although assignments can provide feedback, this feedback may not always be detailed or useful. Instructors may not have the time or resources to provide detailed feedback on every assignment, which can limit the value of the feedback that students receive.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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A system that is used for managing the storage and retrieval of data across multiple interconnected databases is called a Distributed Database Management System(DDBMS) . In this case, the interconnected databases are situated in different geographical areas. In DDBMS, one can access and store data transparently from different geographical locations and ensure high availability, scalability, and fault tolerance mechanisms. DDBMSs are designed to handle huge amounts of data spread across different sites. It can give seamless performance in data sharing and collaboration by organizations.

Features of Distributed DBMS

There is a presence of a certain number of features that make DDBMS very popular in organizing data.

• Data Fragmentation: The overall database system is divided into smaller subsets which are fragmentations. This fragmentation can be three types horizontal (divided by rows depending upon conditions), vertical (divided by columns depending upon conditions), and hybrid (horizontal + vertical).
• Data Replication: DDBMS maintains and stores multiple copies of the same data in its different fragments to ensure data availability, fault tolerance, and seamless performance.
• Data Allocation: It determines if all data fragments are required to be stored in all sites or not. This feature is used to reduce network traffic and optimize the performance.
• Data Transparency: DDBMS hides all the complexities from its users and provides transparent access to data and applications to users.

There are 6 types of DDBMS present there which are discussed below:

• Homogeneous: In this type of DDBMS, all the participating sites should have the exact same DBMS software and architecture which makes all underlying systems consistent across all sites. It provides simplified data sharing and integration.
• Heterogeneous: In this type of DDBMS, the participating sites can be from multiple sites and use different DBMS software, data models, or architectures. This model faces little integration problem as all site’s data representation and query language can be different from each other.
• Federated: Here, the local databases are maintained by individual sites or federations. These local databases are connected via a middleware system that allows users to access and query data from multiple distributed databases. The federation combines different local databases but maintains autonomy at the local level.
• Replicated: In this type, the DDBMS maintains multiple copies of the same data fragment across different sites. It is used to ensure data availability, fault tolerance, and seamless performance. Users can access any data from the nearest replica if the root is down for some reason. However, it is required to perform high-end synchronization of data changes in replication.
• Partitioned: In a Partitioned DDBMS , the overall database is divided into distinct partitions, and each partition is assigned to a specific site. Partitioning can be done depending on specific conditions like date range, geographic location, and functional modules. Each site controls its own partition and the data from other partitions should be accessed through communication and coordination between sites.
• Hybrid: It is just a combination of multiple other five types of DDBMS which are discussed above. The combination is done to address specific requirements and challenges of complex distributed environments. Hybrid DDBMS provides more optimized performance and high scalability.

FAQs on Distributed DBMS

Q1: define distributed dbms.

A Distributed Database Management System(DDBMS) can be defined as a system which controls the storage and retrieval of data among multiple interconnected small databases(nodes). This system is popular in modern data management because of it’s transparency and efficient data sharing mechanism within a distributed environment.

Q2: Write the types of DDBMS?

Distributed Database Management System(DDBMS) can be divided in six types which are–> Homogeneous, Heterogeneous, Federated, Replicated, Partitioned and Hybrid DDBMS where the hybrid DDBMS is the combination of previous five types of DDBMS based on requirements and conditions.

Q3: Write the main difference between Replicated and Partitioned DDBMS?

For Replicated DDBMS, multiple copies of same data fragments are stored and managed across different sites which provides improved availability and performance. In the other hand, for Partitioned DDBMS, the whole database is partitioned into distinct blocks and assigned for different sites.

Q4: What is Federated DDBMS?

In a Federated DDBMS system, all of it’s users can access and query data from multiple distributed databases which are the participants of a single logical database. Federated DDBMS maintains local autonomy and controls over individual sites.

Q5: Why it is difficult for manage a Heterogeneous DDBMS?

In Heterogeneous DDBMS, the participating databases may contain different DBMS software, data modelling and query languages across multiple sites. For this reason managing data representation, translation and query processing may be time consuming and not easy yet.

Q6: What factors should be considered while selecting a DDBMS?

Selecting a DDBMS is a crucial work for business purposes. This selection may depend upon various conditions such as data distribution requirements and availability needs, site autonomy, scalability and performance, the level of heterogeneity in the system etc.

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Technology-Facilitated Violence Against Women Data and Research Consultant

Advertised on behalf of, type of contract :.

Individual Contract

Starting Date :

15-Jun-2024

Application Deadline :

30-May-24 (Midnight New York, USA)

Post Level :

International Consultant

Duration of Initial Contract :

6 months (part time)

Time left :

Languages required :.

English  

Expected Duration of Assignment :

UNDP is committed to achieving workforce diversity in terms of gender, nationality and culture. Individuals from minority groups, indigenous groups and persons with disabilities are equally encouraged to apply. All applications will be treated with the strictest confidence. UNDP does not tolerate sexual exploitation and abuse, any kind of harassment, including sexual harassment, and discrimination. All selected candidates will, therefore, undergo rigorous reference and background checks.

UN Women, grounded in the vision of equality enshrined in the Charter of the United Nations, works for the elimination of discrimination against women and girls; the empowerment of women; and the achievement of equality between women and men as partners and beneficiaries of development, human rights, humanitarian action and peace and security.

UN Women adopts a comprehensive approach to end violence against women and girls (EVAWG) through (1) creating an enabling legal and policy environment, (2) generating evidence, data and knowledge; (3) strengthening survivor-centered responses and access to multi-sectoral, coordinated essential services; (4) preventing VAWG by addressing the causes and drivers of violence against women and girls; (5) partnering with and supporting women’s rights organizations and civil society groups to play a lead role in addressing VAWG. UN Women is now deepening its work on technology-facilitated gender-based violence (TF GBV) to strengthen the global frameworks and standards as well as generating evidence of what works in preventing and eliminating violence against women and girls across the online-offline continuum.

Although online and technology-facilitated GBV share the same causes and many of the drivers of offline forms of violence against women and girls - including structural gender inequality and discrimination, unequal power relations, deeply entrenched cultural and social norms and patterns of harmful masculinities - there are specific features of digital spaces that require dedicated focus and solutions for instance, the scale, speed and ease of internet communication, combined with anonymity, pseudonymity and impunity. Presently, there are significant gaps in knowledge and evidence of what works in preventing TF GBV, which is crucial to inform evidence-based prevention and response frameworks and interventions.

To address these gaps, in 2024 UN Women has strengthened its efforts to address TF GBV, with a focus on strengthening the global frameworks and standards as well as generating evidence of what works in preventing and eliminating violence against women and girls across the online-offline continuum. Through the project: ‘UN Women’s Catalytic Programming to address Technology Facilitated Violence against Women & Girls’, UN Women focuses on addressing existing gaps on TFGBV through a focus on developing global tools and standards as well as on implementing pilot interventions in two countries in the Latin America region: Bolivia and Mexico. UN Women’s work on TFGBV also contributes to the achievement of the targets of the blueprint of the Generation Equality Action Coalition on Gender-Based Violence and the Action Coalition on Technology and Innovation.

In this context, UN Women seeks to hire an international consultant that will support research efforts on TF GBV, to contribute towards enhanced knowledge, data and evidence on what works to prevent and respond to TF GBV. The consultant will be reporting to the Programme Specialist, Violence against Women Data and Research and s/he will be working closely with and supported by other colleagues in the EVAW Section, including the Policy Specialists of the EVAW Section managing different thematic portfolios relevant for this consultancy assignment.

Duties and Responsibilities

Tasks will include:

• Conduct research and analysis to update and finalize the internal repository of TF GBV questions within surveys  
• Identify existing surveys that include specific questions on TF VAW;
• Compile information in the existing internal database;
• Based on collected data and information, produce an analysis that summarizes and illustrates how TFGBV has been addressed in research and surveys over the years, as well as existing gaps.
• Develop a series of materials on TF GBV for UN Women’s Website and external audiences
• Develop a dedicated section on TF GBV to showcase the issue, challenges, and promising practices on UN Women’s Knowledge Portal;
• Develop a section on TF GBV for UN Women’s corporate Website;
• Develop a corporate brief on UN Women’s work on TF GBV.
• Conduct an analysis of TFGBV measures adopted by Governments, through the Global Database on Violence against Women
• Through the Global Database, compile and analyze measures adopted by Governments to address TF GBV;
• Produce a paper that summarizes key findings and recommendations;
• Draft short articles/blog pieces (3) for publication on the UN Women and Women Count websites.

Deliverables

Consultant’s Workplace and Official Travel

This is a home-based consultancy with no official travel envisaged.

Competencies

Required skills and experience.