case study on data warehouse

• 214.617.1389

Data Management

• Cloud Data Management Solutions & Consulting
• Enterprise Cloud Data Management Strategy
• Big Data Consulting
• Data Lake Consulting & Development
• Data Cleansing Services
• Data Integration Services
• Master Data Management (MDM) Consulting

BI & Analytics

• Data Analytics Consulting
• Business Intelligence Consulting Services
• Data Visualization
• Enterprise Business Intelligence (EBI) Consulting Services
• Cloud Business Intelligence Consultants
• On-Site BI Architect Session
• Business Intelligence Audit

Custom Development Services

• Cloud Data Management
• Data Warehouse Modeling
• Enterprise Data Strategy
• Data Lake Consulting
• Data Cleansing
• Integration Services
• Master Data Management Consulting
• Customer Data Platforms Solutions

Support & Training

• AWS Solutions
• Azure Solutions
• Azure Fabric
• Power BI Datamarts
• SAP Business Objects
• Case Studies
• Learning Center

Creating an Enterprise Data Warehouse for a World Leader in the Fluid Motions Industry

Standardizing 42 Disparate ERP Instances and Establishing an  Enterprise Data Warehouse

“Comments have been made about how we haven’t uncovered any issues in the project thus far. That’s unusual for large software related initiatives. That speaks to the upfront architecture and planning and the subsequent execution and partnership from WCI.” 

– VP of IT, Fluid Motions Company

The company (a world leader in manufacturing industrial pumps, valves, actuators, controls, seals and managed services in industries such as power, gas, chemical, water and others) needed help extracting data from several disparate ERP systems. They wanted to extract data from 42 ERP instances, standardize flat files, and get all of the information into one  data warehouse . What complicated the situation even more is that the ERP systems were from all sorts of different vendors (Oracle, SAP, BAAN, Microsoft, PRMS). Another thing that was very important was to have a core set of metrics and a central dashboard that combined all of the information from the various locations around the world.

The project was a result of a surge in demand for enterprise data from the executive level. The company knew that it needed a central repository for information that encompassed all the data from its locations around the world. The requests often came from the top down and when data administrators needed to find the pertinent information, the logistical implications of coordinating the data extraction efforts resulted in a very time-intensive effort. The company knew it needed to standardize data accessibility.

With this increase in demand, along with a desire for consistent analytics, the company decided to search out a partner that could advise and guide their internal team. They also wanted to invest in an Enterprise Data Warehouse with someone who had knowledge of how to consolidate disparate ERP systems.  Due to WCI’s experience with large-scale projects of this nature as well as their delivery and strategy expertise within data warehousing, WCI was chosen for this project.

“We chose WCI to extract and map data for our company because they’ve been around for a while and have a good reputation. They know technology and the business. They speak our language and engage well with ERP system owners.”

– VP of IT, Fluid Motions Company

Planning & Strategy WCI Consulting knew that there was a big project ahead to achieve all of the goals the company had in mind so several phases were planned out over the course of 2 years. WCI architected a roadmap that would take ERP data from 8 main databases and put it into the Enterprise Data Warehouse. This entailed integrating the 5 Oracle ERP instances with the 3 SAP ERPs. Rapid Marts were also implemented in the Oracle ERP systems to improve the flow of the project.

Creating a Team This was a large undertaking so, along with WCI’s resources, WCI helped the client add their own capable resources to this project. This was done in order to build firsthand knowledge within the company and ensure long-term success.

“In the last year, we’ve settled down and established a good team with the help of WCI. 3 years ago, we did not even have a BI team in the company.”

Standard Data Definition Templates One of the main hurdles was that there was no standardization of fields or data definitions across the ERP systems. To fix this issue WCI developed a data services tool to reach into the backend of the database and bring the data forth in a way that could be used. The company now knows what fields to go after and how to establish them each time a new ERP instance is encountered. These data definition templates have been the cornerstone of this project and have completely re-hauled the way the client’s data is treated.

“Now my team completely understands how to pull a new ERP instance with the help of these templates.”

By early 2015 the data from the Tier 1 ERP systems will be ready to be consumed by the business. The company will have 1 common and consistent way to obtain key metrics.

The long-term effect of the project, once completed, will be the ease of flow when it comes to obtaining information. What once was a long and incongruent process to get relevant information at an aggregated level will now be streamlined as all of the pertinent data will be stored in one central data warehouse with one team that controls it.

“WCI has done such good work for us. Their data and business intelligence knowledge is extensive and their integrity and ethics just speak volumes. They’re just very easy and excellent to work with.”

Download PDF Version

“All data demands will go through one common process and team. It will be a streamlined process versus a fire drill that involves multiple teams. With a quick turnaround, executives can get a thorough level of data from the entire enterprise. Access to key metrics will be as easy as a few clicks.”

See what more WCI clients have to say about our services .

Related Posts

Mediant Health Resources Case Study

The Client Mediant Health Resources specializes in providing IT subject matter solutions. Mediant's focus is on driving value through data…

Creating a BI Strategy for An Emergency Healthcare Company

Amalgamating Disparate Systems and Creating a Long-Term BI Strategy for an Emergency Healthcare Company "We were very impressed with the…

Get Started

Want a  FREE  on-site discovery session with your team?  Receive a  FREE data management evaluation session with one of our veteran data architects. Reap the benefits of an expert’s outlook on taking control of your business insights and data. Stop missing out on data-driven opportunities,  and start making smarter, more profitable decisions today.

Practical Data Warehousing: Successful Cases

Table of contents:

No matter how smooth the plan may be in theory, practice will certainly make adjustments. Because each real case has its own characteristics, which in the general case cannot be taken into account. Let's see how the world's leading brands have adapted to their needs a well-known way of storing information — data warehousing. If you think this is your case, then arrange a call .

The Reason for Making Decisions

The need to make business decisions based on data analysis has long been beyond doubt. But to get this data, it needs to be collected, sorted and prepared for analytics .

Operating Supplement

supplier integrations

cost reduction

David Schwarz

DATAFOREST has the best data engineering expertise we have seen on the market in recent years.

This is what data warehousing specialists do. To focus on the best performance, it makes sense to consider how high-quality custom assemblies came out of this constructor.

Data warehousing interacts with a huge amount of data

A data warehousing is a digital storage system that integrates and reconciles large amounts of data from different sources. It helps companies turn data into valuable information and make informed decisions based on it. Data warehousing combines current and historical data and acts as a single source of reliable information for business.

After raw data mining (extract, transform, load) info enters the warehouse from operating systems, such as an enterprise data resource planning system or a customer relationship management system. Sources also include databases, partner operational systems, IoT devices, weather apps, and social media. Infrastructure can be on-premises or cloud-based, with the latter option predominating in recent times.

Data warehousing is necessary not only for storing information, but also for processing structured and unstructured data: video, photos, sensor indicators. Some data warehousing options use built-in analytics and in-memory database data technology (info is stored in RAM rather than on a hard drive). This is necessary to access reliable data in real time.

After data is sorted, it is sent to data marts for further analysis by BI or data science .

Why consider data warehousing cases

Consideration of known options for data warehousing is necessary, first of all, in order not to keep making the same mistakes. Based on a working solution, you can improve your own performance. If you want to always be on the cutting edge of technology, book a call .

• When using data warehouses, executives access data from different sources, they do not have to decide blindly.
• Data warehousing is needed for quick retrieval and analysis. When using warehouses, you can quickly request large amounts of data without involving personnel for this.
• Before uploading to the warehouse, the system creates data cleansing tasks and puts them for further processing, ensuring converting the data into a consistent format for subsequent analyst reports.
• The warehouse contains large amounts of historical data and allows you to study past trends and issues to predict events and improve the business structure.

Blindly repeating other people's decisions is also impossible. Your case is unique and probably requires a custom approach. At best, well-known storage solutions can be taken as a basis. You can do it yourself, or you can contact DATAFOREST specialists for professional services. We have a positive experience and positive customer stories of data warehousing creating and operating.

Case 1: How the Amazon Service Does Data Warehousing

Amazon is one of the world's largest and most successful companies with a diversified business: cloud computing, digital content, and more. As a company that generates vast amounts of data (including data warehousing services), Amazon needs to manage and analyze its data effectively.

Two main businesses

Amazon's data warehousing needs are driven by the company's vast and diverse data sources, which require sophisticated tools and technologies to manage and analyze effectively.

1. One of the main drivers of Amazon's business is its e-commerce platform , which allows customers to purchase a wide range of products through its website and mobile apps. Amazon's data warehousing needs in this area are focused on collecting, storing, and analyzing data related to customer behavior, purchase history, and other metrics. This data is used to optimize Amazon's product recommendations engine, personalize the shopping experience for individual customers, and identify growth strategies.

2. Amazon's other primary business unit is Amazon Web Services (AWS), which offers cloud computing managed services to businesses and individuals. AWS generates significant amounts of data from its cloud data infrastructure, including customer usage and performance data. To manage and analyze this modern data effectively, Amazon relies on data warehousing technologies like Amazon Redshift, which enables AWS to provide real-time analytics and insights to its customers.

3. Beyond these core businesses, Amazon also has significant data warehousing needs in digital content (e.g., video, music, and books). Amazon's advertising business relies on data analysis to identify key demographics and target ads more effectively to specific audiences.

By investing in data warehousing and analytics capabilities, Amazon through digital transformation can maintain its competitive edge and continue to grow and innovate in the years to come.

Do you want to streamline your data integration?

Obstacles on the way to the goal.

Amazon faced several specific implementation details and challenges in its data warehousing efforts.

• The brand needed to integrate data from various sources into a centralized data warehouse. It required the development of custom data pipelines to collect and transform data into a standard format.

• Amazon's data warehousing needs are vast and constantly growing, requiring a scalable solution. The company distributed data warehouse architecture center using technologies like Amazon Redshift, allowing petabyte-scale data storage and analysis.

• As a company that generates big data, Amazon would like to ensure that its data warehousing solution could provide real-time data analytics and insights. Achieving high performance requires optimizing data storage, indexing, and querying processes.

• Amazon stores sensitive customer data in its warehouse, prioritizing data security. To protect against security threats, the brand implements various security measures, including encryption, access controls, and threat detection.

• Building and maintaining a data warehousing solution can be expensive. Amazon leverages cloud-based data warehousing solutions (Redshift) to minimize costs, which provide a cost-effective, pay-as-you-go pricing model.

Amazon's data warehousing implementation required careful planning, significant investment in technology and infrastructure, and ongoing optimization and maintenance to ensure high performance and reliability.

Change for the better

When Amazon considered all the needs, found the right tools, and implemented a successful data warehouse, the company got the following main business outcomes:

• Improved data driven decision

• Better customer enablement

• Cost effective decision

• Improved performance

• Competitive advantage

• Scalability

Amazon's data warehousing implementation has driven the company's growth and success. Not surprisingly, a data storage service provider must understand data storage. The cobbler's children don't need to have no shoes.

Case 2: Data Warehousing Adventure with UPS

United Parcel Services (UPS) is an American parcel delivery and supply chain management company founded in 1907 with an annual revenue of 71 billion dollars and logistics services in more than 175 countries. In addition, the brand distributes goods, customs brokerage, postal and consulting services. UPS processes approximately 300 million tracking requests daily. This effect was achieved, among others, thanks to intelligent data warehousing.

One mile for $50 million

In 2013, UPS stated that it hosted the world's largest DB2 relational database in two United States data centers for global operations. Over time, global operations began to increase, as did the amount of semi structured data. The goal was to use different forms of storage data to make better users business decisions.

One of the fundamental problems was route optimization. According to an interview with the UPS CTO, saving 1 mile a day per driver could save 1.5 million gallons of fuel per year or $50 million in total savings.

However, the data was distributed in DB2; some included repositories, some local, and some spreadsheets. UPS needed to solve the data infrastructure problem first and then optimize the route.

Four letters "V."

The big data ecosystem efficiently handles the four "Vs": volume, validity, velocity, and variety. UPS has experimented with Hadoop clusters and integrated its storage details and computing system into this ecosystem. They upgraded data warehousing and computing power to handle petabytes of data, one of UPS's most significant technological achievements.

The following Hadoop components were used:

• HDFS for storage

• Map Reduce for fast processing

• Kafka streaming

• Sqoop (SQL-to-Hadoop) for ingestion

• Hive & Pig for structured queries on unstructured data

• monitoring system for data nodes and names

But that's just speculation because, due to confidentiality, UPS didn't declassify the tools and technologies they used in their big data ecosystem.

Constellation of Orion

The result was a four-year ORION (On-Road Integrated Optimization and Navigation) route optimization project. Costs — about one billion dollars a year. ORION used the results to data stores and calculate big data and got analytics from more than 300 million data points to optimize thousands of routes per minute based on real-time information. In addition to the economic benefits, the Orion project shortened approximately 100 million shipping miles and a 100,000-ton reduction in carbon emissions.

Case 3: 42 ERP Into One Data Warehouse

In general, the topic of specific cases of data warehousing implementation is sufficiently secret. There may be cases of consent and legitimate interests in the contracts. There are open-source examples of work, but the vast majority are on paid libraries. The subject is so relevant that you can earn money from it. Therefore, sometimes there are "open" cases, but the brand name is not disclosed.

Brand X needs help

World leader in industrial pumps, valves, actuators, controls, etc., needed help extracting data from disparate ERP systems. They wanted it from 42 ERP instances, standardized flat files, and collected all the information in one data warehouse. The ERP systems were from different vendors (Oracle, SAP, BAAN, Microsoft, PRMS) to complicate future matters.

The client also wanted a core set of metrics and a central dashboard to combine all the information from different locations worldwide. The project resulted from a surge in demand for corporate data from database management. The company knew its data warehousing needed a central repository for all data from its locations worldwide. Requests often came from top to bottom, and when an administrator required access to the correct data, there were logistical extracting problems. And the project gets started.

Are you interested in enhanced insights through data aggregation?

The foundation stone.

The hired third-party developer center has made a roadmap, according to which ERP data was taken from 8 major databases and placed in a corporate data warehouse. It entailed integrating 5 Oracle ERP instances with 3 SAP ERP. Rapid Marts have also been integrated into Oracle ERP systems to improve the project's progress.

One of the main challenges was the need for more standardization of fields or operational data definitions in ERP systems. To solve this problem, the contractor has developed a data service tool that allows access to the back end of the database and displays info suitably. Since then, the customer has known which fields to use and how to set them each time a new ERP instance is encountered. These data definition patterns were the project's foundation stone and completely changed how customer data is handled. It was a point to launch consent.

All roads lead to data warehousing

The company has one common and consistent way to obtain critical indicators. The long-term effect of the project is the ease of obtaining information. What was once a long and inconsistent process of getting relevant information at an aggregate level is now streamlined to store data in one central repository with one team controlling it.

Data Warehousing: Different Cases — General Conclusions

Each data warehouse organization has unique methods and tools because business needs differ. In this case, data warehousing can be compared with a mosaic and a children's constructor. You can make different figures from the same parts, arranging the elements especially. And if one part is lost or broken, you need to make a new one or find another one and "process it with a rasp."

Generalities between different cases of data warehousing

There are several common themes and practices among successful data warehousing implementations, including:

• Successful data warehousing implementations start with clearly understanding the business objectives and how the warehouse (or data lake) can support those objectives.

• The data modeling process is critical to the success of data warehousing.

• The data warehouse is only as good as the data it contains.

• Successful data warehousing requires efficient data integration processes that can operate large volumes of data and ensure consistency and accuracy.

• Data warehousing needs ongoing performance tuning to optimize query performance.

• A critical factor in data warehousing is a user-friendly interface that makes it easy for end users to access the data and perform complex queries and analyses.

• Continuous improvement is essential to ensure the data warehouse remains relevant and valuable to the business.

Competent data warehousing implementations combine technical expertise and a deep understanding of business details and user needs.

Your case is not mentioned anywhere

When solving the problem of organizing data warehousing , one would like to find a description of the same case and do everything according to plan. But the probability of this event is negligible — you will have to adapt to the specifics of the customer's business and consider your knowledge and capabilities, as well as the technical and financial conditions of the project. Then it would help if you took a piece of the puzzle or parts of the constructor and built your data warehouse. Minus — you have to work. Plus — it will be your decision on data storage and only your implementation.

Data Warehousing Is Like a Trampoline

Changes in data warehousing , like any technological and methodological changes, are carried out to improve the data collection, storage, and analysis level. It takes the customer to a new level in his activity and the contractor — to his own. Like a jumper and a trampoline: separately, it is just a gymnast and just equipment, and in combination, they give a certain third quality — the possibility of a sharp rise.

If you are faced with the problem of organizing a new data warehousing system, or you are simply interested in what you read, let's exchange views with DATAFOREST.

What is the benefit of data warehousing for business?

A data warehouse is a centralized repository that contains integrated data from various sources and systems. Data warehousing provides several benefits for businesses: improved decision-making, increased efficiency, better customer insights, operational efficiency, and competitive advantage.

What is the definition of a successful data warehousing implementation?

The specific definition of a successful data warehouse implementation will vary depending on the goals of the organization and the particular use case for data warehousing. Some common characteristics are: meeting business requirements, high data quality, scalability, user adoption, and positive ROI.

What are the general considerations for implementing data warehousing?

Implementing data warehousing involves some general considerations: business objectives, data sources, quality and modeling, technology selection, performance tuning, user adoption, ongoing maintenance, and support.

What are the most famous examples of the implementation of data warehousing?

There are many famous examples of the implementation of data warehousing across industries:

• Walmart has one of the largest data warehousing implementations in the world

• Amazon's data warehousing solution is known as Amazon Redshift

• Netflix uses a data warehouse to store and analyze data from its streaming platform

• Coca-Cola has a warehouse to consolidate data from business units and analyze it

• Bank of America analyzes customer data by data warehousing to improve customer experience

What are the challenges while implementing data warehousing, and how to overcome them?

Based on the experiences of organizations that have implemented data warehousing, some common challenges and solutions are:

• Ensuring the quality of the data that is being stored and analyzed. You must establish data quality standards and implement data validation and cleansing by data types.

• Integrating from disparate data sources. Establishing a clear data integration strategy that considers the different data sources, formats, and protocols involved is vital.

• As the amount of data stored in a data warehouse grows, performance issues may arise. A brand should regularly monitor query performance and optimize the data warehouse to ensure that it remains efficient and effective.

• To ensure that sensitive data stored in the data warehouse is secure. It involves implementing appropriate measures such as access controls, encryption, and regular security audits. They are details of privacy security.

• Significant changes to existing processes and workflows. Solved by establishing a transparent change management process that involves decision-makers and users at all levels.

What is an example of how successful data warehousing has affected a business?

An example of how successful data warehousing has affected Amazon is its recommendation engine. It suggests products to customers based on their browsing and purchasing history. By using artificial intelligence and machine learning algorithms to analyze customer data, Amazon has improved the fully managed accuracy of its recommendations, resulting in increased sales and customer satisfaction.

What role does data integration play in data warehousing?

Data integration is critical to data warehousing, enabling businesses to consolidate and standardize data from multiple sources, ensure data quality, and establish effective data governance practices.

How are data quality and governance tracked in data warehousing?

Data quality and governance are tracked in data warehousing through a combination of data profiling, monitoring, and management processes and establishing data governance frameworks that define policies and procedures for managing data quality and governance. So, businesses can ensure that their data is accurate, consistent, and compliant with regulations, enabling effective decision-making and driving business applications' success.

Are there any measures to the benefits of data warehousing?

The benefits of business data warehousing can be measured through improvements in data quality, efficiency, decision-making, revenue and profitability, and customer satisfaction. By tracking these metrics, businesses can assess the effectiveness of their data warehousing initiatives and make informed decisions about future investments in data management and analytics with cloud services.

How to avoid blunders when warehousing data?

By following the best practices, businesses can avoid common mistakes, minimize the risk of blunders when warehousing data, and ensure their data warehousing initiatives are successful and practical to be analyzed with business intelligence.

Aleksandr Sheremeta

Get More Value!

You will get from us best tailored content that will help your business grow.

latest posts

Data science tools: a business decision depends on the choice, devops service provider: building software faster, better, cheaper, multimodal ai: training neural networks for a unified understanding, media about us, when it comes to automation, choosing the right partner has never been more important, 15 most innovative database startups & companies, 10 best web development companies you should consider in 2022, try to trying.

Never give up

We love you to

People like this

Success stories

Web app for dropshippers.

hourly users

Shopify stores

Financial Intermediation Platform

model accuracy

timely development

E-commerce scraping

manual work reduced

pages processed daily

DevOps Experience

QPS performance

Supply chain dashboard

system integrations

More publications

Data Warehouse Gives Complete Picture of a Business

Mastering IoT Data Integration: Improving Business Operations and Security

Empower Your Business: Achieve Efficiency and Security with SaaS Data Integration

Let data make value

We’d love to hear from you.

Share the project details – like scope, mockups, or business challenges. We will carefully check and get back to you with the next steps.

Thanks for your submission!

Ready to grow?

Share your project details, and let’s explore how we can achieve your goals together.

Stay a little longer
and explore what we have to offer!

Build fully managed real-time data pipelines in minutes.

Explore the deployment options for Estuary Flow: Public, Private, and BYOC, with a comparison.

Replaced Fivetran with trouble-free CDC

Streaming CDC to Snowflake

Streaming CDC to BigQuery built 6x faster

Reliable CDC & outstanding support

Real-Time Data Warehouse Examples (Real World Applications)

Discover how businesses are leveraging real-time data warehouses to gain actionable insights, make informed decisions, and drive growth.

Gone are the days when organizations had to rely on stale, outdated data for their strategic planning and operational processes. Now,  real-time data warehouses process and analyze data as it is generated, helping overcome the limitations of their traditional counterparts. The impact of real-time data warehousing is far-reaching. From eCommerce businesses to healthcare providers, real-time data warehouse examples and applications span various sectors.

The significance of real-time data warehousing becomes even more evident when we consider the sheer volume of data being generated today. The global data sphere is projected to reach a staggering  180 zettabytes by 2025 . 

With these numbers, it’s no wonder every company is looking for solutions like real-time data warehousing for managing their data efficiently. However, getting the concept of a real-time data warehouse, particularly when compared with a traditional data warehouse, can be quite intimidating, even for the best of us. 

In this guide, with the help of a range of examples and real-life applications, we will explore how real-time data warehousing can help organizations across different sectors overcome the data overload challenge.

What Is A Real-Time Data Warehouse?

Image Source

A  Real-Time Data Warehouse (RTDW) is a  modern tool for data processing that provides immediate access to the most recent data. RTDWs use real-time  data pipelines to transport and collate data from multiple data sources to one central hub, eliminating the need for batch processing or outdated information.

Despite similarities with traditional data warehouses, RTDWs are capable of  faster data ingestion and processing speeds . They can detect and rectify errors instantly before storing the data, providing consistent data for an effective decision-making process.

• Real-Time Data Warehouse Vs Traditional Data Warehouse

Traditional data warehouses act as storage centers for  accumulating an organization’s historical data from diverse sources. They combine this varied data into a unified view and provide comprehensive insights into the past activities of the organization. However, these  insights are often outdated by the time they are put to use , as the data could be days, weeks, or even months old.

On the other hand, real-time data warehousing brings a significant enhancement to this model by  continuously updating the data they house. This dynamic process provides a current snapshot of the organization’s activities at any given time, enabling immediate analysis and action. 

Let’s look at some of the major differences between the two.

Complexity & Cost

RTDWs are  more complex and costly to implement and maintain than traditional data warehouses. This is because they require more advanced technology and infrastructure to handle real-time data processing.

Decision-Making Relevance

Traditional data warehouses predominantly assist in long-term strategic planning. However, the real-time data updates in RTDWs make them  suitable for both immediate, tactical decisions and long-term strategic planning.

Correlation To Business Results

Because of fresher data availability, RTDWs make it easier to  connect data-driven insights with real business results and provide immediate feedback.

Operational Requirements

RTDWs demand constant data updates, a process that can be carried out without causing downtime in the data warehouse operations . Typically, traditional warehouses don't need this feature but it becomes crucial when dealing with data updates happening every week.

Data Update Frequency

While the lines between traditional data warehouses and real-time data warehouses are now blurred due to some data warehouses adopting streaming methods to load data, traditionally, the former updated their data in batches on a daily, weekly, or monthly schedule. As a result, the data some of these data warehouses hold may not reflect the most recent state of the business. In contrast, real-time data warehouses  update their data almost immediately as new data arrives.

• 3 Major Types Of Data Warehouses

Let's take a closer look at different types of data warehouses and explore how they integrate real-time capabilities.

Enterprise Data Warehouse (EDW)

An Enterprise Data Warehouse (EDW) is a  centralized repository that stores and manages large volumes of structured and sometimes unstructured data  from various sources within an organization. It serves as a comprehensive and unified data source for business intelligence, analytics, and reporting purposes. The EDW consolidates data from multiple operational systems and transforms it into a consistent and standardized format.

The EDW is designed to  handle and scale with large volumes of data . As the organization's data grows over time, the EDW can accommodate the increasing storage requirements and processing capabilities. It also acts as a  hub for integrating data from diverse sources across the organization . It gathers information from operational systems, data warehouses, external sources, cloud-based platforms, and more.

Operational Data Store (ODS)

An Operational Data Store (ODS) is designed to  support operational processes and provide real-time or near-real-time access to current and frequently changing data. The primary purpose of an ODS is to facilitate operational reporting, data integration, and data consistency across different systems. 

ODS collects data from various sources, like transactional databases and external feeds, and  consolidates it in a more user-friendly and business-oriented format.  It typically stores detailed and granular data that reflects the most current state of the operational environment. 

A  Data Mart is a specialized version of a data warehouse that is  designed to meet the specific analytical and reporting needs of a particular business unit , like sales, marketing, finance, or human resources.

Data Marts provide a more targeted and simplified view of data. It contains a  subset of data that is relevant to the specific business area , organized in a way that facilitates easy access and analysis.

Data Marts are  created by extracting, transforming, and loading (ETL) data from the data warehouse or other data sources and structuring it to support analytical needs. They can include pre-calculated metrics, aggregated data, and specific dimensions or attributes that are relevant to the subject area.

11 Applications Of Real-Time Data Warehouses Across Different Sectors 

The use of RTDWs is now common across many sectors. The rapid access to information they provide significantly improves the operations of many businesses, from online retail to healthcare.

Let’s take a look at some major sectors that benefit from these warehouses for getting up-to-the-minute data.

In the dynamic eCommerce industry, RTDWs facilitate immediate data processing that is used to get insights into customer behavior, purchase patterns, and website interactions. This enables marketers to  deliver personalized content, targeted product recommendations, and swift customer service . Additionally, real-time inventory updates help maintain optimal stock levels, minimizing overstock or stock-out scenarios.

RTDWs empower AI/ML algorithms with new, up-to-date data. This ensures models make predictions and decisions based on the most current state of affairs. For instance, in automated trading systems, real-time data is critical for  making split-second buying and selling decisions.

Manufacturing & Supply Chain

RTDWs support advanced manufacturing processes such as  real-time inventory management, quality control, and predictive maintenance . It provides crucial support for business intelligence operations. You can make swift adjustments in production schedules based on instantaneous demand and supply data to  optimize resource allocation and reduce downtime.

RTDWs in healthcare help improve care coordination. It provides  instant access to patient records, laboratory results, and treatment plans, improving care coordination . They also support real-time monitoring of patient vitals and provide immediate responses to critical changes in patient conditions.

Banking & Finance 

In banking and finance, RTDWs give you the  latest updates on customer transactions, market fluctuations, and risk factors . This real-time financial data analysis helps with immediate fraud detection, instantaneous credit decisions, and real-time risk management.

• Financial Auditing

RTDWs enable continuous auditing and monitoring to give auditors  real-time visibility into financial transactions . It helps identify discrepancies and anomalies immediately to enhance the accuracy of audits and financial reports.

• Emergency Services

RTDWs can keep track of critical data like the  location of incidents, available resources, and emergency personnel status . This ensures an efficient deployment of resources and faster response times, potentially saving lives in critical situations.

• Telecommunications

RTDWs play a vital role in enabling efficient network management and enhancing overall customer satisfaction. They provide  immediate analysis of network performance, customer usage patterns, and potential system issues . This improves service quality, optimizes resource utilization, and proactive problem resolution.

• Online Gaming

RTDWs provide  analytics on player behaviors, game performance, and in-game purchases  to support online gaming platforms. This enables game developers to promptly adjust game dynamics, improve player engagement, and optimize revenue generation.

• Energy Management

In the energy sector, RTDWs provide  instantaneous data on energy consumption, grid performance, and outage situations. This enables efficient energy distribution, quick response to power outages, and optimized load balancing.

• Cybersecurity

RTDWs are crucial for cybersecurity as they provide  real-time monitoring of network activities and immediate detection of security threats. This supports swift countermeasures, minimizes damage, and enhances the overall security posture.

Real-Time Data Warehouse: 3 Real-Life Examples For Enhanced Business Analytics

To truly highlight the importance of real-time data warehouses, let’s discuss some real-life case studies.

• Case Study 1: Beyerdynamic 

Beyerdynamic , an audio product manufacturer from Germany, was facing difficulties with its previous method of analyzing sales data . In this process, they extracted data from their legacy systems into a spreadsheet and then compiled reports, all manually. It was time-consuming and often caused inaccurate reports.  

To overcome these challenges, Beyerdynamic developed a  data warehouse that automatically extracted transactions from its existing ERP and financial accounting systems. This data warehouse was carefully designed to store standard information for each transaction, like product codes, country codes, customers, and regions. 

They also implemented a web-based reporting solution that helped managers create their standard and ad-hoc reports based on the data held in the warehouse.

Supported by an optimized data model, the new system allowed the company to perform detailed sales data analyses and identify trends in different products or markets.

• Production plans could be adjusted quickly based on changing demand , ensuring the company neither produced excessive inventory nor missed out on opportunities to capitalize on increased demand.
• With the new system, the company could use  real-time data for performance measurement and appraisal . Managers compared actual sales with targets by region, assessed the success of promotions, and quickly responded to any adverse variances.
• Sales and distribution strategies could be quickly adapted according to changing demands in the market. For instance, when gaming headphone sales started increasing in Japan, the company promptly responded with tailored promotions and advertising campaigns.
• Case Study 2: Continental Airlines 

Continental Airlines is a major player in the aviation world. It  faced significant issues because of old, manual systems. Their outdated approach slowed down decision-making and blocked easy access to useful data from departments like customer service, flight operations, and financials. Also, the lack of real-time data meant that decisions were often based on outdated information.

They devised a robust plan that hinged on 2 key changes: the  ‘Go Forward’ strategy and a  ‘real-time data warehouse’

• Go Forward Strategy:  This initiative focused on tailoring the airline’s services according to the customer’s preferences. The concept was simple but powerful –  understand what the customer wants and adapt services to fit that mold . In an industry where customer loyalty can swing on a single flight experience, this strategy aims to ensure satisfaction and foster brand loyalty.
• Real-Time Data Warehouse:  In tandem with the new strategy, Continental also implemented an RTDW. This technological upgrade gave the airline quick access to current and historical data. The ability to extract insights from this data served as a vital reference point for strategic decision-making , optimizing operations, and enhancing customer experiences.

The new strategy and technology led to critical improvements:

• The airline could offer a personalized touch by understanding and acting on customer preferences. This  raised customer satisfaction and made the airline a preferred choice for many.
• The introduction of the RTDW brought simplicity and efficiency to the company’s operations. It facilitated quicker access to valuable data which was instrumental in  reducing the time spent on managing various systems. This, in turn, resulted in significant cost savings and increased profitability.
• Case Study 3: D Steel 

D Steel, a prominent steel production company, was facing a unique set of challenges when they aimed to  set up a real-time data warehouse to analyze their operations. While they tried to use their existing streams package for synchronization operations, several obstacles emerged.

The system was near real-time but it  couldn't achieve complete real-time functionality.  The load on the source server was significantly high and synchronization tasks required manual intervention.

More so, it lacked automation for  Data Definition Language (DDL) , compatibility with newer technologies, and had  difficulties with data consistency verification, recovery, and maintenance . These challenges pushed the steel company to seek a new solution.

The Solution

D Steel decided to implement real-time data warehouse solutions that enabled instant data access and analysis. 

The new RTDWs system proved to be extremely successful as it resolved all previous problems. It provided:

• Real-time synchronization
• Implementing DDL automation
• Automated synchronization tasks
• Reduced the load on the source server

The system also introduced a unique function that  compared current year data with that of the previous year  and helped the company in annual comparison analysis.

• Enhancing Real-Time Data Warehousing: The Role of Estuary Flow

Estuary’s Flow is our  data operations platform   that binds various systems by a central  data pipeline . With Flow, you get diverse systems for storage and analysis, like databases and data warehouses. Flow is pivotal in  maintaining synchronization amongst these systems, ensuring that new data feeds into them continuously.

Flow utilizes  real-time data lakes as an integral part of its data pipeline. This serves dual roles. 

First, it works as a transit route for data and facilitates an easy flow and swift redirection to distinct storage endpoints. This feature also helps in backfilling data from these storage points.

The  secondary role of the data lake in Flow is to serve as a reliable storage backbone . You can lean on this backbone without the fear of turning into a chaotic ‘data swamp.’ 

Flow assures automatic organization and management of the  data lake . As data collections move through the pipeline, Flow applies different schemas to them as per the need.

Remember that the data lake in Flow doesn’t replace your ultimate storage solution. Instead, it aims to  synchronize and enhance other storage systems crucial for powering key workflows , whether they're analytical or transactional.

As we have seen with real-time data warehouse examples, this solution transcends industry boundaries. Only those organizations that embrace real-time data warehousing to its fullest can unlock the true potential of their data assets. 

While it can be a little tough to implement, the benefits of real-time data warehousing far outweigh the initial complexities, and the long-term advantages it offers are indispensable in today's data-driven world.

If you’re considering setting up a real-time data warehouse, investing in a top-notch real-time data ingestion pipeline like  Estuary Flow should be your first step. Designed specifically for building real-time data management, Flow provides a no-code solution to synchronize your many data sources and integrate fresh data seamlessly.  Signup for Estuary Flow for free and seize the opportunity today.

Start streaming your data for free

Table of Contents

• Manufacturing & Supply Chain
• Banking & Finance 

Popular Articles

ChatGPT for Sales Conversations: Building a Smart Dashboard

Why You Should Reconsider Debezium: Challenges and Alternatives

Don't Use Kafka as a Data Lake. Do This Instead.

Streaming pipelines., simple to deploy., simply priced..

Real World Data Warehousing Examples: Use Cases and Applications

We’re really beginning to experience another industrial revolution. That is, we’re actively entering into the ‘Age of Data.’ As you look at your own life, business, and world around you - you’ll quickly notice that so much of it is now connected in some way. And, soon, our society will become persistently connected as we spread connectivity even further across the globe.

A recent report from IDC indicates these key trends around data:

• The evolution of data from business background to life-critical . Once siloed, remote, inaccessible, and mostly underutilized, data has become essential to our society and our individual lives. In fact, IDC estimates that by 2025, nearly 20% of the data in the global datasphere will be critical to our daily lives and nearly 10% of that will be hypercritical.
• Embedded systems and the Internet of Things (IoT) . As standalone analog devices give way to connected digital devices, the latter will generate vast amounts of data that will, in turn, allow us the chance to refine and improve our systems and processes in previously unimagined ways. Big Data and metadata (data about data) will eventually touch nearly every aspect of our lives—with profound consequences. By 2025, an average connected person anywhere in the world will interact with connected devices nearly 4,800 times per day—basically one interaction every 18 seconds.
• Mobile and real-time data . Increasingly, data will need to be instantly available whenever and wherever anyone needs it. Industries around the world are undergoing "digital transformation" motivated by these requirements. By 2025, more than a quarter of data created in the global datasphere will be real time in nature, and real-time IoT data will make up more than 95% of this.

That being said, it’s important to understand how you can gather, quantify, and actually analyze this information. Coupled with solutions around data analytics and big data processing, data warehousing allows you to take valuable information to an entirely new level. From there, powerful data warehouse solutions help you create data visualization to make better decisions around your business and the market.

But, we’re getting a bit ahead of ourselves. Let’s define data warehousing, look at some use-cases, and discuss a few best practices.

• What is a data warehouse?  At a very high level, a data warehouse is a system that pulls together data from many different sources within an organization for reporting and analysis. From there, the reports created from complex queries within a data warehouse are used to improve business efficiency, make better decisions, and even introduce competitive advantages. It’s important to note that a data warehouse is definitely different than a traditional database. Sure, data warehouses and databases are both relational data systems, but they were definitely built to serve different purposes. A data warehouse is built to store large quantities of historical data and enable fast, complex queries across all the data, typically using Online Analytical Processing (OLAP). A database was built to store current transactions and enable fast access to specific transactions for ongoing business processes, known as Online Transaction Processing (OLTP).

So, data warehousing allows you to aggregate data, from various sources. This data, typically structured, can come from Online Transaction Processing (OLTP) data such as invoices and financial transactions, Enterprise Resource Planning (ERP) data, and Customer Relationship Management (CRM) data. Finally, data warehousing focuses on data relevant for business analysis, organizes and optimizes it to enable efficient analysis.

• How are data warehouses used?  Unlike databases and other systems which simply ‘store’ data, data warehousing takes an entirely different approach. Let me give you a few examples and uses. Data warehouses normally use a denormalized data structure , which uses fewer tables because it groups data and doesn’t exclude data redundancies. Denormalization offers better performance when reading data for analytical purposes. On that note, data warehouses are used for business analysis, data and market analytics, and business reporting. Data warehouses typically store historical data by integrating copies of transaction data from disparate sources. Data warehouses can also use real-time data feeds for reports that use the most current, integrated information.

Here’s the other cool part when it comes to use-cases, the structure of data warehouses makes analytical queries much simpler to perform. No advanced knowledge of database applications is required. Analytics in data warehouses is dynamic, meaning it takes into account data that changes over time.

Finally, the cloud. While a traditional data warehouse implementation can sometimes be a very expensive project, SaaS solutions are taking data warehousing to a new level. New cloud-based tools allow enterprises to setup a data warehouse in days, with no upfront investment, and with much greater scalability, storage and query performance.

• Bottom tier—database server used to extract data from multiple sources
• Middle Tier—OLAP server, which transforms data to enable analysis and complex queries
• Top Tier—tools used for high-level data analysis, querying, reporting, and data mining

So, when creating your own data warehousing architecture, follow these three tiers to help identify data points, how you'll analyse them, and what the visualization will look like.

From there, data warehouses are usually structured using one of the following models:

• Virtual data warehouse—a set of separate databases, which can be queried together, forming one virtual data warehouse.
• Data mart—small data warehouses set up for business-line specific reporting and analysis. An organization's data marts together comprise the organization's data warehouse.
• Enterprise data warehouse (EDW)—a large data warehouse holding aggregated data that spans the entire organization.
• Cloud-based data warehouse—imagine everything you need from a data warehouse, but hosted in the cloud. Cloud-based data warehouse architectures can typically perform complex analytical queries much faster because they are massively parallel processing (MPP).

As you take this all in, remember the one big point I made earlier in the blog. You don’t need to do this all alone. Good partners can help you establish a date baseline and really understand the type of data warehouse architecture you require. From there, you really begin to unleash the power of data as you analyze vast amounts of information and help visualize it for your business.

Bill Kleyman

Bill Kleyman, Director of Technology Solutions at EPAM Industry Analyst | Board Advisory Member | Writer/Blogger/Speaker | Executive | Millennial | Techie Bill Kleyman brings more than 15 years of experience to his role as Director of Technology Solutions at EPAM. Using the latest innovations, such as AI, machine learning, blockchain, DevOps, cloud and advanced technologies, Mr. Kleyman delivers solutions to customers that help them achieve their business goals and remain competitive in their market. An active member in the technology industry, he was ranked #16 globally in the Onalytica study that reviewed the top 100 most influential individuals in the cloud landscape; and #4 in another Onalytica study that reviewed the industry’s top Data Security Experts. Mr. Kleyman enjoys writing, blogging and educating colleagues about everything related to technology. His published and referenced work can be found on WindowsITPro, Data Center Knowledge, InformationWeek, NetworkComputing, TechTarget, DarkReading, Forbes, CBS Interactive, Slashdot and more.

Also Check Out

Get panoply updates on the fly., work smarter, better, and faster with monthly tips and how-tos..