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What Are Some Real-World Examples of Big Data?

Since our first ancestors put ink to parchment, data has been part of the human experience.

From tracking the complex movements of the planets, to more basic things like bookkeeping, data has shaped the way we’ve evolved. Today, thanks to the internet, we collect such vast amounts of data that we have a whole new term to describe it: “big data.”

While big data is not only collected online, the digital space is undoubtedly its most abundant source. From social media likes, to emails, weather reports, and wearable devices, huge amounts of data are created and accumulated every second of every day. But how exactly is it used?

If you’re just starting out from scratch, then try this  free data short course on for size.

In this article, I’ll focus on some of the most big data examples out there. These are ways in which organizations—large and small—use big data to shape the way they work.

• What is big data and why is it useful?
• Big data in marketing and advertising
• Big data in education
• Big data in healthcare
• Big data in travel, transport, and logistics
• Key takeaways

First, let’s start with a quick summary of what big data is, and why so many organizations are scrambling to harness its potential.

1. What is big data and why is it useful?

“Big data” is used to describe repositories of information too large or complex to be analyzed using traditional techniques. For the most part, big data is unstructured, i.e. it is not organized in a meaningful way.

Although the term is commonly used to describe information collected online, to understand it better, it can help to picture it literally. Imagine walking into a vast office space without desks, computers, or filing cabinets. Instead, the whole place is a towering mess of disorganized papers, documents, and files. Your job is to organize all of this information and to make sense of it. No mean feat!

While digitization has all but eradicated the need for paper documentation, it has actually increased the complexity of the task. The skill in tackling big data is in knowing how to categorize and analyze it. For this, we need the right big data tools  and know-how. But how do we categorize such vast amounts of information in a way that makes it useful?

While this might seem like a fruitless task, organizations worldwide are investing huge amounts of time and money in trying to tap big data’s potential. This is why data scientists and data analysts are currently so in demand.

Learn more about it in our complete guide to what is big data .

But how is it done? Let’s take a look.

2. Big data in marketing and advertising

One of big data’s most obvious uses is in marketing and advertising. If you’ve ever seen an advert on Facebook or Instagram, then you’ve seen big data at work. Let’s explore some more concrete examples.

Netflix and big data

Netflix has over 150 million subscribers, and collects data on all of them. They track what people watch, when they watch it, the device being used, if a show is paused, and how quickly a user finishes watching a series.

They even take screenshots of scenes that people watch twice. Why? Because by feeding all this information into their algorithms, Netflix can create custom user profiles. These allow them to tailor the experience by recommending movies and TV shows with impressive accuracy.

And while you might have seen articles about how Netflix likes to splash the cash on new shows , this isn’t done blindly—all the data they collect helps them decide what to commission next.

Amazon and big data

Much like Netflix, Amazon collects vast amounts of data on its users. They track what users buy, how often (and for how long) they stay online, and even things like product reviews (useful for sentiment analysis ).

Amazon can even guess people’s income based on their billing address. By compiling all this data across millions of users, Amazon can create highly-specialized segmented user profiles.

Using predictive analytics , they can then target their marketing based on users’ browsing habits. This is used for suggesting what you might want to buy next, but also for things like grouping products together to streamline the shopping experience.

McDonald’s and big data

Big data isn’t just used to tailor online experiences. A good example of this is McDonald’s, who use big data to shape key aspects of their offering offline, too. This includes their mobile app, drive-thru experience, and digital menus.

With its own app, McDonald’s collects vital information about user habits. This lets them offer tailored loyalty rewards to encourage repeat business. But they also collect data from each restaurant’s drive-thru, allowing them to ensure enough staff is on shift to cover demand. Finally, their digital menus offer different options depending on factors such as the time of day, if any events are taking place nearby, and even the weather.

So, if it’s a hot day, expect to be offered a McFlurry or a cold drink…not a spicy burger!

3. Big data in education

Until recently, the approach to education was more or less one-size-fits-all. With companies now harnessing big data, this is no longer the case. Schools, colleges, and technology providers are all using it to enhance the educational experience.

Reducing drop-out rates with big data

Purdue University in Indiana was an early adopter of big data in education. In 2007, Purdue launched a unique, early intervention system called Signals, which was designed to help predict academic and behavioral issues.

By applying predictive modeling to student data (e.g. class prep, level of engagement, and overall academic performance) Purdue was able to accurately forecast which students were at risk of dropping out. When action was required, both students and teachers were informed, meaning the college could intervene and tackle any issues. As a result, according to one study, those taking two or more Signals courses were 21% less likely to drop out.

Improving the learner experience with big data

Some educational technology providers use big data to enhance student learning. One example of this is the UK-based company, Sparx , who created a math app for school kids. Using machine learning, personalized content, and data analytics, the app helps improve the pupil learning experience.

With over 32,000 questions, the app uses an adaptive algorithm to push the most relevant content to each student based on their previous answers. This includes real-time feedback, therefore tackling mistakes as soon as they arise. Plus, by collecting data from all their users across schools, Sparx gains broader insight into the overall learning patterns and pitfalls that students face, helping them to constantly improve their product.

Improving teaching methods with big data

Other educational technology providers have used big data to improve teaching methods. In Roosevelt Elementary School in San Francisco, teachers use an analytics app called DIBELS . The app gathers data on children’s reading habits so that teachers can see where they most need help.

Aggregating data on all pupils, teachers can group those with the same learning needs, targeting teaching where it’s most needed. This also encourages educators to reflect on their methods. For instance, if they face similar issues across multiple students, they might need to adapt their approach.

4. Big data in healthcare

From pharmaceutical companies to medical product providers, big data’s potential within the healthcare industry is huge. Vast volumes of data inform everything from diagnosis and treatment, to disease prevention, and tracking.

Electronic health records and big data

Our medical records include everything from our personal demographics to our family histories, diets, and more. For decades, this information was in a paper format, limiting its usefulness.

However, health systems around the world are now digitizing these data, creating a substantial set of electronic health records (EHRs). EHRs have vast potential. On a day-to-day level, they allow doctors to receive reminders or warnings when a patient needs to be contacted (for instance, to check their medication).

However, EHRs also allow clinical researchers to spot patterns between things like disease, lifestyle, and environment—correlations that would previously have been impossible to detect. This is revolutionizing how we detect, prevent, and treat disease, informing new interventions, and changes in government health policy.

Big data and wearable devices

Healthcare providers are always seeking new ways to improve patient care with faster, cheaper, more effective treatments. Wearables are a key part of this. They allow us to track patient data in real-time.

For instance, a heart monitor worn to detect blood pressure can allow doctors to track patients for extended periods at home, rather than relying on the results of a quick hospital test. If there’s a problem, doctors can quickly intervene. More importantly though, using big data analytics tools, information collected from countless patients can offer invaluable insights, helping healthcare providers improve their products. This ultimately saves money and lives.

Big data for disease tracking

Another application of big data in healthcare is disease tracking. The current coronavirus pandemic is a perfect example. Since the coronavirus outbreak began, governments have been scrabbling to launch track-and-trace systems to stem the spread of disease.

In China, for instance, the government has introduced heat detectors at train stations to identify those with fever. Because every passenger is legally required to use identification before using public transport, authorities can quickly alert those who may have been exposed. The Chinese government also uses security cameras and mobile phone data to track those who have broken quarantine. While this does come with privacy concerns, China’s approach nevertheless demonstrates the power of big data.

5. Big data in travel, transport, and logistics

From flying off on vacation to ordering packages to your front door, big data has myriad applications in travel, transport, and logistics. Let’s explore further.

Big data in logistics

Tracking warehouse stock levels, traffic reports, product orders, and more, logistics companies use big data to streamline their operations. A good example is UPS. By tracking weather and truck sensor data, UPS learned the quickest routes for their drivers.

This itself was a useful insight, but after analyzing the data in more detail, they made an interesting discovery: by turning left across traffic, drivers were wasting a lot of fuel . As a result, UPS introduced a ‘no left turn’ policy. The company claims that they now use 10 million gallons less gas per year, and emit 20,000 tonnes less carbon dioxide. Pretty impressive stuff!

Big data and city mobility

Big data is big business in urban mobility, from car hire companies to the boom of e-bike and e-scooter hire. Uber is an excellent example of a company that has harnessed the full potential of big data. Firstly, because they have a large database of drivers, they can match users to the closest driver in a matter of seconds.

But it doesn’t stop there. Uber also stores data for every trip taken. This enables them to predict when the service is going to be at its busiest, allowing them to set their fares accordingly. What’s more, by pooling data from across the cities they operate in, Uber can analyze how to avoid traffic jams and bottlenecks. Cool, huh?

Big data and the airline industry

Aircraft manufacturer, Boeing, operates an Airplane Health Management System. Every day, the system analyzes millions of measurements across their entire fleet. From in-flight metrics to mechanical analysis, the resulting data has numerous applications.

For instance, by predicting potential failures, the company knows when servicing is required, saving them thousands of dollars annually on unnecessary maintenance. More importantly, this big data provides invaluable safety insights, improving airplane safety at Boeing, and across the airline industry at large.

6. Big data in finance and banking

Fraud detection with big data.

Banks and financial institutions process billions of transactions daily—in 2022 there were more than 21,510 credit card transactions per second ! With the rise of online banking, mobile payments, and digital transactions, the risk of fraud has also increased.

Big data analytics can help in detecting unusual patterns or behaviors in transaction data. For instance, if a credit card is used in two different countries within a short time frame, it might be flagged as suspicious. By analyzing vast amounts of transaction data in real-time, banks can quickly detect and prevent fraudulent activities.

Personalized banking with big data

With over 78% of Americans banking digitally , banks are increasingly using big data to offer personalized services to their customers. By analyzing a customer’s transaction history, browsing habits, and even social media activities, banks can offer tailored financial products, interest rates, or even financial advice.

For instance, if a bank notices that a customer is frequently spending on travel, they might offer them a credit card with travel rewards or discounts.

7. Big data in agriculture

Precision farming with big data.

Farmers are using big data to make more informed decisions about their crops. How do they achieve this? Well, with sensors placed in fields measure the moisture levels, temperature, and soil conditions, as well as on tractors and other farm machinery.

Speaking of farm machinery, here’s an unusual but not for long example: d rones . By equipping drones with cameras can provide detailed aerial views of the crops, helping in detecting diseases or pests. Hobby drone giant DJI already produces its own line of drones for this purpose.

By analyzing this data, farmers can determine the optimal time to plant, irrigate, or harvest their crops, leading to increased yields and reduced costs.

Supply chain optimization with big data

Agricultural supply chains are complex, with multiple stages from farm to table. Big data can help in tracking and optimizing each stage of the supply chain. For instance, by analyzing data from transportation vehicles, storage facilities, and retail outlets, suppliers can ensure that perishable goods like fruits and vegetables are delivered in the shortest time, reducing wastage and ensuring freshness.

These examples can be integrated into the article to provide a more comprehensive overview of the diverse applications of big data across different sectors.

8. Key takeaways

In this post, we’ve explored big data’s real-world uses in several industries. Big data is regularly used by:

• Advertisers and marketers —to tailor offers and promotions, and to make customer recommendations
• Educational institutions —to minimize drop-outs, offer tailored learning, and to improve teaching methods
• Healthcare providers —to create new treatments, develop wearable devices, and to improve clinical research
• Transport and logistics —to streamline supply chain operations, improve airline safety, and even to save fuel and reduce carbon emissions
• Banking and finance —to help prevent fraud, as well as to offer customers tailored products based on their activity
• Agriculture —to help farmers perform as efficiently as possible and to monitor their crops

This taster of big data’s potential highlights just how powerful it can be. From financial services to the food industry, mining and manufacturing, big data insights are shaping the world we live in. If you want to be a part of this incredible journey, and are curious about a career in data analytics, why not try our free, five-day data analytics short course ?

Keen to explore further? Check out the following:

• How To Become A Data Consultant: A Beginner’s Guide
• Bias in Machine Learning: What Are the Ethics of AI?
• What Are Large Language Models? A Complete Guide

30 Big Data Examples and Applications

Check out these examples of how companies use big data to predict the next big step.

By helping companies uncover hidden patterns and trends, big data is now used in nearly every industry to plan future products, services and more. As of 2022, in fact, approximately 97 percent of businesses are investing in big data’s growing power.

At its best, though, big data grounds and enhances human intuition.

These companies are using big data to shape industries from marketing to cybersecurity and much more.

Big Data Examples to Know

• Marketing:  forecast customer behavior and product strategies.
• Transportation: assist in GPS navigation, traffic and weather alerts.
• Government and public administration: track tax, defense and public health data.
• Business: streamline management operations and optimize costs.
• Healthcare: access medical records and accelerate treatment development.
• Cybersecurity: detect system vulnerabilities and cyber threats.

Big Data Examples in Marketing 

Big data and marketing go hand-in-hand, as businesses harness consumer information to forecast market trends, buyer habits and other company behaviors. All of this helps businesses determine what products and services to prioritize.

Location: Marina del Rey, California

System1 develops software to support streamlined, effective digital marketing operations. The company uses AI, machine learning and data science to power its response acquisition marketing platform, known as RAMP, which helps brands engage high-intent customers at the right time.

Location: Los Angeles, California

VALID is VideoAmp ’s big data and technology engine that’s designed to power solutions for ensuring ad content reaches target audiences and measuring ad performance across TV, streaming and digital platforms, while still respecting consumer privacy. The company’s offerings are designed to serve agencies, brands and publishers.

Centerfield

Location: Los Angeles, California 

Getting more information on customers is a great way to discover their desires and how to meet them. Centerfield analyzes customer data to uncover new insights into customer behavior, which influences the marketing and sales techniques it recommends to clients. The company is able to use this information to discover new customers that fit the same patterns as existing customers.

Location: San Mateo, California

At 3Q/DEPT , big data underpins strategies that blend search engine, social, mobile and video marketing . The in-house Decision Sciences team perfects the mix of marketing channels by studying data on transactions, consumer behavior and more, using multi-touch attribution . This big data-informed technique allows analysts to distinguish between effective and ineffective ad impressions on a micro level.

Location: Glendale, California 

With insight help from big data, DISQO offers products for measuring brand and customer experience. The company specializes in research and marketing lift (sales) efforts, providing API and optimization software for tracking key performance and outcome metrics. Over 125 marketing firms utilize DISQO research tools, while over 300 firms utilize its lift solutions.

Location: Seattle, Washington 

Like Facebook and Google, Amazon got sucked into the adtech business by the sheer amount of consumer data at its disposal. Since its founding in 1994, the company has collected reams of information on what millions of people buy, where those purchases are delivered and which credit cards they use. In recent years, Amazon has begun offering more and more companies — including marketing companies — access to its self-service ad portal, where they can buy ad campaigns and target them to ultra-specific demographics, including past purchasers.

Marketing Evolution

Location: New York, New York 

Marketing Evolution pulls data from hundreds of online and offline sources to create detailed consumer profiles that encompass beliefs, location and purchasing habits as well as environmental data like current local weather conditions. Analysts then use a software stack dubbed the “ROI Brain” to craft targeted campaigns where every element, from the messaging itself to the channel it arrives through, reflects individual users’ preferences.

Big Data Examples in Transportation 

Navigation apps and databases, whether used by car drivers or airplane pilots, frequently rely on big data analytics to get users safely to their destinations. Insights into routes, travel time and traffic are pulled from several data points and provide a look at travel conditions and vehicle demands in real time.

Location: Fully remote

Vizion provides shipping container tracking for freight companies, using multiple data sources to keep close tabs on thousands of ships, containers, railways and ports around the world. Using geocodes for locations and facilities, it can provide GPS coordinates to shippers and cargo owners, logistics service providers and freight forwarders across ocean and rail.

Location: Chicago, Illinois 

FourKites ’ platform uses GPS and a host of other location data sources to track packages in real time, whether they’re crossing oceans or traveling by rail. A predictive algorithm then factors in data on traffic, weather and other external factors to calculate the estimated times of arrival for packages, so FourKites clients can give customers advance warning about delays and early deliveries — while also avoiding fees.

Location: San Francisco, California 

As a rideshare company, Uber monitors its data in order to predict spikes in demand and variations in driver availability. That information allows the company to set the proper pricing of rides and provide incentives to drivers so the necessary number of vehicles are available to keep up with demand. Data analysis also forms the basis of Uber’s estimated times of arrival predictions, which goes a long way toward fulfilling customer satisfaction.

Location: Fairfield, Connecticut 

GE ’s Flight Efficiency Services, adopted in 2015 by Southwest Airlines and used by airlines worldwide, can optimize fuel use, safety and more by analyzing the massive volumes of data airplanes generate. How massive? One transatlantic flight generates an average of 1,000 gigabytes . GE’s scalable aviation analytics takes it all in, crunching numbers on fuel efficiency, weather conditions, and passenger and cargo weights.

HERE Technologies

The experts at HERE Technologies leverage location data in several ways, most notably in the HD Live Map , which feeds self-driving cars the layered, location-specific data they need. The map pinpoints lane boundaries and senses a car’s surroundings. Thanks to data from intelligent sensors, the map can see around corners in a way the human eye can’t. And a perpetual stream of intel from fleets of roaming vehicles helps the map warn drivers about lane closures miles away.

Big Data Examples in Government

To stay on top of citizen needs and other executive duties, governments may look toward big data analytics. Big data helps to compile and provide insights into suggested legislation, financial procedure and local crisis data, giving  authorities an idea of where to best delegate resources.

RapidDeploy

Location: Fully Remote

RapidDeploy is a public safety company that creates reporting and analytics software and operates a data platform for emergency response centers. Using AI and big data to increase location accuracy and situational awareness, RapidDeploy’s products are meant to offer insights about how to find callers faster, improve emergency care and reduce response time.

Location: New York, New York

RapidSOS funnels emergency-relevant data to first responders out on 911 calls. Thanks to partnerships with Apple, Android providers and apps like Uber, the company can pull relevant data from patients’ phones and wearables in crisis situations. Free to public safety offices, Clearinghouse integrates into pre-existing call-taking and dispatch channels so the data — including GPS location data and real-time sensor data — reaches EMTs more reliably and securely.

Big Data Examples in Business

Succeeding in business means companies have to keep track of multiple moving parts — like sales, finances, operations — and big data helps to manage it all. Using data analytics, professionals can follow real-time revenue information, customer demands and managerial tasks to not only run their organization but also continually optimize it.

Arity is a data and analytics firm that works in the automotive insurance space, sourcing data from nearly 30 million connected devices. Operating independently under the umbrella of the Allstate Insurance Corporation, it uses AI to analyze driver behavior on behalf of local governments and insurance providers, who then use its data and insights to make pricing and policy decisions. 

Location: Fully Remote Enigma ’s big data analysis platform takes vast data sets of information ranging from merchant transactions and financial health indicators to identity and firmographic information. It then returns insights on private businesses, guiding its clients’ B2B decision making. These data-driven insights are so more accurate than previous methods of investigations into areas like financial health, for example. As a result, only applications that are likely to be approved will be sent forward in the application process, which can lead to increased approval rates on loans.  

Location: San Francisco, California

Forge provides tech, data and marketplace services for the private securities market. Private securities, which include privately traded equities, fractional loans and derivatives, are traded between individuals rather than on an exchange the way publicly traded stocks are. The Forge Intelligence app uses big data to allow users to see real-time trading activity and pricing information in the private market.

The PC-based Skupos platform pulls transaction data from 15,000 convenience stores nationwide. Over the course of a year, that adds up to billions of transactions that can be dissected using the platform’s business analytics tools. Store owners can use the insights to determine location-by-location bestsellers and set up predictive ordering . Distributors, meanwhile, can forecast demand, and brands can analyze a constant influx of product sales data.

Companies often scatter their data across various platforms, but Salesforce is all about cohesion. Their customer relationship management platform integrates data from various facets of a business, like marketing, sales, and services, into a comprehensive, single-screen overview. The platform’s analytics provide automatic AI-informed insights and predictions on metrics like sales and customer churn . Users can also connect Salesforce with outside data management tools rather than toggling between multiple windows.

Location: Los Gatos, California 

The premise of Netflix ’s first original TV show — the David Fincher-directed political thriller House of Cards — had its roots in big data. Netflix invested $100 million in the first two seasons of the show, which premiered in 2013, because consumers who watched House of Cards also watched movies directed by David Fincher and starring Kevin Spacey . Executives correctly predicted that a series combining all three would be a hit. 

Today, big data impacts not only which series Netflix invests in, but how those series are presented to subscribers. Viewing histories, including the points at which users hit pause in any given show, reportedly influence everything from the thumbnails that appear on their homepages to the contents of the “Popular on Netflix” section

Big Data Examples in Healthcare

When it comes to medical cases, healthcare professionals may use big data to determine the best treatment. Patterns and insights can be drawn from millions of patient data records, which guide healthcare workers in providing the most relevant remedies for patients and how to best advance drug development.

Location: Chicago, Illinois

Kalderos is a healthtech company building solutions to support compliant drug discount programs. Its platform brings together data from multiple sources to identify and resolve noncompliance and improve transparency and collaboration among stakeholders. Kalderos says its technology has identified more than $1 billion in noncompliance so that organizations across the healthcare landscape can avoid revenue losses and focus their efforts on serving patients.

Tempus ’ tablet-based tool has made file cabinets of medical records portable and accessible in real time. Designed to inform physicians’ decisions during appointments, Tempus trawls huge digital archives of clinical notes, genomic data, radiology scans and more to turn out data-driven treatment recommendations . These recommendations are personalized, too, though — based on data from past cases in which patients had similar demographic traits, genetic profiles and cancer types.

SOPHiA GENETICS

Location: Boston, Massachusetts 

SOPHiA GENETICS provides data solutions for healthcare professionals based on big data metrics, with specializations in oncology, inherited diseases and biopharmacy. The company’s SOPHiA DDM platform provides multimodal insights from clinical, biological, genomics and radiomics datasets for screening and diagnosis purposes. Sophia Genetics’ technology has analyzed over one million genomic profiles, and intends to provide future insight support for data relating to proteomics, metabolomics and more.

Propeller Health

Location: Madison, Wisconsin

Propeller Health reimagined the inhaler as an IoT gadget. Widely used for the treatment of asthma and other chronic obstructive pulmonary diseases, the sensor-equipped inhalers export data to a smartphone app that tracks inhaler use, as well as environmental factors like humidity and air quality. Over time, in-app analytics can help identify possible flare-up triggers and produce reports that patients can share with their doctors.

Location: Eschborn, Hessen, Germany and San Francisco, California

Innoplexus ’ Ontosight life sciences data library, featuring search tools rooted in AI and blockchain technology , was compiled to help pharmaceutical researchers sift more quickly through relevant data and streamline drug development. A truly massive repository, it includes everything from unpublished PhD dissertations to gene profiles to a whopping 26 million pharmaceutical patents.

Big Data Examples in Cybersecurity

As cyber threats and data security concerns persist, big data analytics are used behind the scenes to protect customers every day. By reviewing multiple web patterns at once, big data can help identify unusual user behavior or online traffic and defend against cyber attacks before they even start.

Location: Foster City, California

Cyber attacks are so sophisticated and prevalent that it’s hard for the research into prevention to catch up. Luckily, big data can provide some of the same insights by analyzing patterns in cyber attacks and recommending strategies for staying safe. Exabeam analyzes data from companies that have suffered attacks to help companies build models of what common attacks look like and how to detect and deter them before they are successful.

Splunk ’s Security Operations Suite relies on big data to identify and respond to cybersecurity threats and fraud. Systemwide data flows through Splunk’s analytics tools in real time, allowing it to pinpoint anomalies with machine learning algorithms. Splunk’s data-driven insights also help it prioritize concurrent breaches, map out multipart attacks and identify potential root causes of security issues.

Own Company

Location: Englewood Cliffs, New Jersey 

Own  is a cloud-based platform for data security, backup, archiving and sandbox seeding. Using big data insights, the software provides automated backups and security risk metrics for Salesforce, Microsoft and ServiceNow data environments. Own has partnered with AWS, nCino and Veeva to provide data protection and compliance services for businesses across the country .

Arista Networks

Location: Santa Clara, California

Arista ’s Awake Security platform works a bit like the human brain. Sensors scan data where it’s stored, whether in the cloud or embedded in an IoT device. Much as our nerves relay information back to our brain, Awake’s sensors port key findings back to the Awake Nucleus, a centralized deep learning center that can detect threats and parse the intent behind unusual data. 

In certain cases, it’s used in collaboration with a network of human cybersecurity experts who are up to date on the latest cyber attack techniques and industry-specific protocols.

Exterro Inc.

Location: Beaverton, Oregon

Exterro ’s Forensic Toolkit , or FTK, stores enterprise-scale data in a straightforward database structure, processing and indexing it up front. In an emergency situation, that allows for quicker searches that are further accelerated through the use of distributed processing across an array of computers. FTK makes full use of its hardware resources, focusing all of its available processing power on extracting evidence that clients can leverage in civil and criminal cases.

Brennan Whitfield, Margo Steines and Tammy Xu contributed reporting to this story.

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Big data tutorial: a step-by-step guide, what is big data and what are its benefits, top big data applications across industries, how to become a big data engineer, exploring the main components of big data: a comprehensive overview, big data projects, 10 mind-blowing big data projects revolutionizing industries.

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Table of Contents

The accurate approximations of the current scenario suggest that internet users worldwide create 2.5 quintillion bytes of data daily. A big data project involves collecting, processing, evaluating, and interpreting large volumes of data to derive valuable insights, styles, and trends. These projects frequently require specialized tools and techniques to handle the demanding situations posed by the sheer quantity, velocity, and diversification of data. They may be used throughout numerous domains, like business, healthcare, and finance, to make informed selections and gain a deeper understanding of complicated phenomena related to large volumes of data .

What is a Big Data Project?

A big data project is a complicated task that emphasizes harnessing the ability of large and diverse datasets. The key factors that provide advanced information about what a big data project includes:

• Volume, Velocity, and Variety 
• Data Storage 
• Data Processing
• Data Integration 
• Data Analysis and Mining
• Scalability and Parallel Processing
• Data Visualization
• Privacy and Security 
• Cloud Computing
• Domain Applications

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Why is a Big Data Project Important?

A big data project encompasses convoluted procedures of acquiring, managing, and analyzing large and numerous datasets, regularly exceeding the abilities of conventional data processing techniques. It entails stages like data sourcing, storage design, ETL operations , and application of specialized analytics tools, which include big data projects with source code like Hadoop, and Spark. The project's favorable outcomes rely on addressing challenges like data quality , scalability, and privacy concerns. The insights gained can cause advanced decision-making, predictive modeling, and enhanced operational performance. Effective big data projects require a blend of domain understanding, data engineering talents, and a strategic method to address information at an unprecedented scale.

Top 10 Big Data Projects

1. google bigtable.

Google's Bigtable is an enormously scalable and NoSQL database system designed to handle large quantities of data whilst keeping low-latency performance. It is used internally at Google to power various offerings, which include Google Search, Google Analytics, and Google Earth and manage big data analytics projects.

Key Features

• Bigtable can manage petabytes of records dispensed across heaps of machines, making it appropriate for dealing with massive datasets.This big data project idea has successfully managed massive amounts of datasets. 
• It gives low-latency read-and-write operations, making it suitable for real-time applications.

2. NASA’s Earth Observing System Data and Information System (EOSDIS)

EOSDIS is a comprehensive gadget that collects, records, and distributes Earth science statistics from NASA's satellites, airborne sensors, and other devices. It aims to offer researchers, scientists, and the general public admission to diverse environmental statistics.

• EOSDIS encompasses various data facilities, each specializing in unique Earth science information, along with land, ocean, and atmosphere. 
• These data facilities ensure that the information amassed is saved, managed, and made accessible for research and analysis, contributing to our expertise on Earth's structures and weather.

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3. Facebook's Hive

Hive is a data warehousing infrastructure constructed on top of Hadoop, designed for querying and coping with big data projects using a SQL-like language referred to as HiveQL. 

• Without programming knowledge, it lets users analyze information stored in Hadoop's HDFS (Hadoop Distributed File System). 
• Hive translates HiveQL queries into MapReduce jobs, making it simpler for data analysts and engineers to work with big information.
•  It supports partitioning, bucketing, and diverse optimization strategies to improve performance. 

4. Netflix's Recommendation System

Netflix's recommendation system employs big data analytics and machine learning to customize content material pointers for its users. 

• By reading customer conduct, viewing history, rankings, and alternatives, the machine indicates films and TV shows that align with customer tastes. 
• This complements consumer engagement and retention; as it facilitates customers discovering content material they might enjoy.
• Netflix's recommendation engine uses an aggregate of collaborative filtering, content-based filtering, and deep knowledge of algorithms to improve its accuracy and effectiveness.

5. IBM Watson

IBM Watson is an AI-powered platform that uses big data projects, analytics, natural language processing , and machine learning to understand and process unstructured statistics. It has been carried out in numerous domains, including healthcare, finance, and customer service.

• Watson's talents include language translation, sentiment analysis, image recognition, and question-answering. 
• It can process large quantities of data from diverse resources, documents, articles, and social media to extract significant insights and provide appropriate recommendations. 
• IBM Watson demonstrates the potential of big data technology in enabling advanced AI programs and reworking industries through data-driven choice-making.

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6. Uber's Movement

Uber's Movement project is a superior instance of how big data projects are utilized in urban mobility evaluation. It uses anonymized ride information from Uber trips to offer insights into site visitor patterns and transportation traits in towns and cities. 

• The records from Uber movements can help urban planners, town officers, and researchers make informed decisions about infrastructure upgrades, site visitors management, and public transportation plans. 
• Uber Movement provides entry to aggregated and anonymized statistics via visualizations and datasets, bearing in mind a higher knowledge of site visitors and congestion dynamics in different urban areas.

7. CERN's Large Hadron Collider(LHC) 

The Large Hadron Collider (LHC) at CERN is the sector's largest and most effective particle accelerator. It generates huge quantities of data in the course of particle collision experiments. To manage and examine this data, CERN employs advanced huge records technologies. 

• Distributed computing and grid computing architectures method, the large datasets generated by experiments, allow scientists to find new particles and gain insights into essential physics standards.
• The records generated using the LHC pose substantial demanding situations due to its volume and complexity, showcasing how big data processing is crucial for current scientific research.

8. Twitter's Real-time Analytics

In real-time, Twitter's real-time analytics leverage big data processing to screen, analyze, and visualize tendencies, conversations, and personal interactions. This lets corporations, researchers, or even the general public gain insights into what is occurring on the platform. 

• By processing and studying huge amounts of tweets and user engagement facts, Twitter becomes aware of trending topics, sentiment analysis, and consumer conduct styles.
• This real-time data aids in understanding public sentiment, monitoring events, and improving marketing techniques.

9. Walmart's Data Analytics

Walmart, one of the world's largest stores, notably uses data analytics to optimize various operations elements. Big data analytics enables Walmart to make information-driven choices from stock control to supply chain optimization, pricing techniques, and customer conduct analysis. 

• It helps ensure efficient inventory tiers, minimize wastage, improve client experiences, and enhance standard commercial enterprise performance. 
• Walmart's data analytics efforts showcase how big data can transform conventional retail practices, resulting in good-sized enhancements in diverse operational areas.

10. City of Chicago's Array of Things

The City of Chicago's Array of Things big data project is a network of sensor nodes deployed throughout the metropolis to gather information on various environmental elements, air quality, temperature, and humidity. 

• This assignment pursuits to offer real-time statistics for urban planning and decision-making. By studying this big data, town officials can make informed selections about infrastructure upgrades, public protection, and typical quality of life. 
• The Array of Things assignment exemplifies how the internet of things and big data technologies can contribute to growing smarter and more sustainable towns.

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1. What are some common challenges in big data projects?

Common challenges in big data projects include:

• Handling data quality.
• Ensuring scalability.
• Coping with data security and privacy.
• Managing diverse data formats.
• Addressing hardware and infrastructure constraints.
• Locating efficient approaches to processing and examining massive volumes of data.

2. What are some widely used big data technologies?

Some widely used big data technologies consist of Hadoop (and its environment components like HDFS, MapReduce, and Spark), NoSQL databases (together with MongoDB, Cassandra), and distributed computing frameworks (like Apache Flink).

3. How do I choose the right tools for my big data project?

To pick the right tool, consider your project's necessities. Evaluate elements like data volume, velocity, variety, and the complexity of analyses required. Cloud solutions provide scalability, whilst open-source tools like Hadoop and Spark are flexible for use instances. Choose tools that align together with your team's skill set and finances. 

4. What skills are needed for a successful big data project?

A successful big data project calls for a blend of competencies. In conclusion, interpreting  one's main  learning style and choosing suitable platforms for personal growth  can significantly decorate the effectiveness of the learning manner.Big data project topics covering Data engineering competencies, such as data acquisition, ETL strategies, and data cleansing, are essential. Programming proficiency (e.g., Python, Java) for records processing and analysis is important. Knowledge of big data technology, including Hadoop and Spark, is useful. Statistical and machine learning talents help in deriving insights from data. Additionally, problem-solving teamwork is precious for interpreting consequences in a meaningful context.

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Real-World MLOps Examples: End-To-End MLOps Pipeline for Visual Search at Brainly

In this second installment of the series “Real-world MLOps Examples,” Paweł Pęczek , Machine Learning Engineer at Brainly , will walk you through the end-to-end Machine Learning Operations (MLOps) process in the Visual Search team at Brainly . And because it takes more than technologies and processes to succeed with MLOps, he will also share details on: 

• 1 Brainly’s ML use cases,
• 2 MLOps culture,
• 3 Team structure,
• 4 And technologies Brainly uses to deliver AI services to its clients,

Enjoy the article!

Disclaimer: This article focuses on the setup of mostly production ML teams at Brainly.

Check the previous article from this series

Real-World MLOps Examples: Model Development in Hypefactors

Company profile

Brainly is the leading learning platform worldwide, with the most extensive Knowledge Base for all school subjects and grades. Hundreds of millions of students, parents, and teachers use Brainly every month because it is a proven way to help them understand and learn faster. Their Learners come from more than 35 countries. 

The motivation behind MLOps at Brainly 

To understand Brainly’s journey toward MLOps, you need to know the motivation for Brainly to adopt AI and machine learning technologies. At the time of this writing, Brainly has hundreds of millions of monthly users across the globe. With that scale of active monthly users and the number of use cases they represent, ML applications can benefit users greatly from Brainly’s educational resources and improve their learning skills and paths.

Brainly’s core product is Community Q&A Platform where users can ask any question from any school subject by:

• Typing it out 
• Taking a photo of the question
• Saying it out loud

Once a user enters their input, the product provides the answer with step-by-step explanations. If the answer is not in the Knowledge Base already, Brainly sends it to one of the Community Members to respond. 

“We build AI-based services at Brainly to boost the educational features and take them to the next level—this is our main reasoning behind taking advantage of the tremendous growth of AI-related research.” — Paweł Pęczek, Machine Learning Engineer at Brainly

The AI and technology teams at Brainly use machine learning to provide Learners with personalized, real-time learning help and access to the world’s best educational products. The objectives of the AI/ML teams at Brainly are to:

• Move from a reactive to a predictive intervention system that personalizes their users’ experience
• Solve future educational struggles for users ahead of time
• Make students more successful in their educational paths

You can find more on Brainly’s ML story in this article .

Machine learning use cases at Brainly

The AI department at Brainly aims to build a predictive intervention system for its users. Such a system leads them to work on several use cases around the domains of:

• Content : Extracting content attributes (e.g., quality attributes) and metadata enrichment (e.g., curriculum resource matching)
• Users : Enhancing the learning profile of the users
• Visual Search : Parsing images and converting camera photos into answerable queries
• Curriculum : Analyzing user sessions and learning patterns to build recommender systems

It would be challenging to elaborate on the MLOps practices for each team working on these domains, so in this article, you will learn how the Visual Search AI team does real-world MLOps.

Watch this video to learn how the Content AI team does MLOps.

“If you think about how users of Brainly’s services formulate their search queries, you may find that they tend to lean towards methods of input that are easy to use. This includes not only visual search but also voice and text search with special kinds of signals that can be explored with AI.“ — Paweł Pęczek, Machine Learning Engineer at Brainly

MLOps team structure

The technology teams at Brainly are divided into product and infrastructure teams. The infrastructure team focuses on technology and delivers tools that other teams will adapt and use to work on their main deliverables. 

On top of the teams, they also have departments. The DevOps and Automation Ops departments are under the infrastructure team. The AI/ML teams are in the services department under infrastructure teams but related to AI, and a few AI teams are working on ML-based solutions that clients can consume.

On the foundation of the AI department is the ML infrastructure team, which standardizes and provides solutions for the AI teams that can be adapted. The ML infrastructure team makes it easy for the AI teams to create training pipelines with internal tools that make their workflow easier by providing templated solutions in the form of infrastructure-as-a-code for each team to autonomously deploy in their own environments.

Multiple AI teams also contribute to ML infrastructure initiatives. This is similar to an internal open-source system where everyone works on the tools they maintain.

“This setup of teams, where we have a product team, an infrastructure team that divides into various departments, and internal teams working on specific pieces of technology to be exposed to the product, is pretty standard for big tech companies.” — Paweł Pęczek, Machine Learning Engineer at Brainly

Bookmark for later

How to Build ML Model Training Pipeline

The MLOps culture at Brainly

Two main philosophies behind the MLOps culture at Brainly are:

• 1 Prioritizing velocity
• 2 Cultivating collaboration, communication, and trust

Prioritizing velocity 

“The ultimate goal for us is to enable all of the essential infrastructure-related components for the teams, which should be reusable. Our ultimate goal is to provide a way for teams to explore and experiment, and as soon as they find something exciting, push that into clients’ use cases as soon as possible.” — Paweł Pęczek, Machine Learning Engineer at Brainly

The goal for the MLOps ecosystem is to move as quickly as possible and, over time, learn to build automated components faster. Brainly has common initiatives under the umbrella of its infrastructure team in AI departments. Those initiatives enable teams to grow faster by focusing on their main deliverables. 

“Generally, we try to be as fast as possible, exposing the model to real-world traffic. Without that, the feedback loop would be too long and bad for our workflow. Even from the team’s perspective, we usually want this feedback instantly—the sooner, the better. Otherwise, this iterative process of improving models takes too much time.” — Paweł Pęczek, Machine Learning Engineer at Brainly

Effects of prioritizing velocity: How long does it take the team to deploy one model to production?

During the early days, when they had just started the standardization initiative, each team had various internal standards and workflows, which made it take months to deploy one model to production. With workflows standardized across teams and data in the right shape, most teams are usually ready to deploy their model and embed it as a service in a few weeks—if research goes well, of course. 

“The two phases that take the most time at the very beginning are collecting meaningful data and labeling the data. If the research is entirely new and you have no other projects to draw conclusions from or base your understanding on, the feasibility study and research may take a bit longer.  Say the teams have the data and can immediately start the labeling. In that case, everything goes smoothly and efficiently in setting up the experimentation process and building ML pipelines—this happens almost instantly. They can produce a similar-looking code structure for that project. Maintenance is also pretty easy.” — Paweł Pęczek, Machine Learning Engineer at Brainly

Another pain point that teams faced was structuring the endpoint interface so clients could adopt the solution quickly. It takes time to talk about and agree on the best interface, and this is a common pain point in all fields, not just machine learning. They had to cultivate a culture of effective collaboration and communication.

Cultivating collaboration, communication, and trust

After exposing AI-related services, the clients must understand how to use and integrate them properly. This brings interpersonal challenges, and the AI/ML teams are encouraged to build good relationships with clients to help support the models by telling people how to use the solution instead of just exposing the endpoint without documentation or telling them how.

Brainly’s journey toward MLOps

Since the early days of ML at Brainly, infrastructure, and engineering teams have encouraged data scientists and machine learning engineers working on projects to use best practices for structuring their projects and code bases. 

With that, they can get started quickly and will not need to pay a large amount of technical debt in the future. These practices have evolved as they have built a more mature MLOps workflow following the “ maturity levels ” blueprint.

“We have quite an organized transition between various stages of our project development, and we call these stages ‘maturity levels.’” — Paweł Pęczek, Machine Learning Engineer at Brainly

The other practice they imposed from the onset was to make it easy for AI/ML teams to begin with pure experimentation. At this level, the infrastructure teams tried not to impose too much on the researchers so they could focus on conducting research, developing models, and delivering them.

Setting up experiment tracking early on is a best practice 

“We enabled experiment tracking from the beginning of the experimentation process because we believed it was the key factor significantly helping the future reproducibility of research.” — Paweł Pęczek, Machine Learning Engineer at Brainly

The team would set up research templates for data scientists to bootstrap their code bases for special use cases. Most of the time, these templates have all the modules that integrate with their experiment tracking tool, neptune.ai . 

They integrate with neptune.ai seamlessly with code, such that everything is nicely structured in terms of the reports that they send to neptune.ai, and teams can review and compare experiments pre- and post-training. 

→ Case study on how Brainly added the experiment tracking component to their MLOps stack.

→ Lessons learned by engineers behind neptune.ai when building an experiment tracking tool.

MLOps maturity levels at Brainly

Mlops level 0: demo app.

When the experiments yielded promising results, they would immediately deploy the models to internal clients. This is the phase where they would expose the MVP with automation and structured engineering code put on top of the experiments they run. 

“We are using the internal automation tools we already have to make it easy to show our model endpoints. We are doing this so clients can play with the service, exposing the model so they can decide whether it works for them. Internally, we called this service a ‘demo app’.” — Paweł Pęczek, Machine Learning Engineer at Brainly

During the first iterations of their workflow, the team made an internal demo application that clients could connect to through code or a web UI (user interface) to see what kind of results they could expect from using the model. It was not a full-blown deployment in a production environment.

“Based on the demo app results, our clients and stakeholders decide whether or not to push a specific use case into advanced maturity levels. When the decision comes, the team is supposed to deploy the first mature or broad version of the solution, called ‘release one.’ On top of what we already have, we assembled automated training pipelines to train our model repetitively and execute the tasks seamlessly.” — Paweł Pęczek, Machine Learning Engineer at Brainly

MLOps level 1: Production deployment with training pipelines

As the workflows for experimentation and deployment got better and became standard for each team, they shifted their focus to ensuring they had a good approach to re-training their model when new data arrived.

The use cases evolved eventually, and as the amount of new data exploded, the team switched to a data-centric AI approach, focusing on collecting datasets and constantly pushing them into pipelines instead of trying to make the models perfect or doing too much research.

Because speed was important in their culture, they were expected to use automated tools to send full deployments to the production environment. With these tools, they could do things like:

• Trigger pipelines that embedded models as a service
• Verify that the model’s quality did not degrade compared to what they saw during training

“We expose our services to the production environment and enable monitoring to make sure that, over time, we can observe what happens. This is something we call MLOps maturity level one (1) .”  — Paweł Pęczek, Machine Learning Engineer at Brainly

The goal of working at this level is to ensure that the model is of the highest quality and to eliminate any problems that could arise early during development. They also need to monitor and see changes in the data distribution ( data drift, concept drift , etc.) while the services run.

MLOps level 2: Closing the active learning loop

MLOps level two (2) was the next maturity level they needed to reach. At this level, they would move the model to a more mature level where they could close the active learning loop if it proved to have a good return on investment (ROI) or was needed for other reasons related to their KPIs and the vision of the stakeholders. 

They would continually create larger and better data sets by automatically extracting data from the production environment, cleaning it up, and, if necessary, sending it to a labeling service. These datasets would go into the training pipelines they have already set up. They would also implement more extensive monitoring with better reports sent out daily to ensure that everything is in order.

Machine learning workflow of the Visual Search team

Here’s a high-level overview of the typical ML workflow on the team:

• First, they would pull raw data from the producers (events, user actions in the app, etc.) into their development environment
• Next, they would manipulate the data, for instance, by modulating the filter and preprocessing it into the required formats
• Depending on how developed the solution was, they would label the datasets, train the models using the training pipeline, or leave them as research models

“When our model is ready, we usually evaluate it. Once approved, we start an automated deployment pipeline and check again to ensure the model quality is good and to see if the service guarantees the same model quality measured during training. If that’s the case, we simply deploy the service and monitor to see if something is not working as expected. We validate the problem and act upon it to make it better.  We hope to push as many use cases as possible into this final maturity level, where we have closed the active learning cycle and are observing whether or not everything is fine.” — Paweł Pęczek, Machine Learning Engineer at Brainly

Of course, closing the loop for their workflow requires effort and time. Also, some use cases will never reach that maturity level because it is natural that not every idea will be valid and worth pursuing to that level.

MLOps infrastructure and tool stack for Brainly’s Visual Search team

The team’s MLOps infrastructure and tool stack is divided into different components that all contribute to helping them ship new services fast:

• 2 Experimentation and model development
• 3 Model testing and validation
• 4 Model deployment
• 5 Continuous integration and delivery
• 6 Monitoring

The image below shows an overview of the different components and the tools the team uses:

Let’s take a deeper look at each component.

Data infrastructure and tool stack for Brainly’s visual search team

“Our data stack varies from one project to another. On the computer vision team, we try to use the most straightforward solutions possible. We simply store the data in S3, and that’s just fine for us, plus permissions prohibiting unauthorized users from mutating data sets as they are created.” — Paweł Pęczek, Machine Learning Engineer at Brainly

The team has automated pipelines to extract raw data and process it in the format they want it to be trained on. They try to be as generic as possible with data processing without sophisticated tools. They built on what the Automation Ops team had already developed to integrate with the AWS tech stack.

The team uses AWS Batch and Step Functions to run batch processing and orchestration. These simple solutions focus more on the functionalities they know best at Brainly than on how the service works. 

“Our current approach gets the job done, but I wouldn’t say it’s extremely extensive or sophisticated. I know that other teams use data engineering and ETL processing tools more than we do, and compared to them, we use more straightforward solutions to curate and process our data sets.” — Paweł Pęczek, Machine Learning Engineer at Brainly

Experimentation infrastructure and tool stack for Brainly’s visual search team

“We try to keep things as simple as possible for experimentation. We run training on EC2 instances and AWS SageMaker in their most basic configuration. For the production pipelines, we add more steps, but not too many, so that SageMaker doesn’t get overused.” — Paweł Pęczek, Machine Learning Engineer at Brainly

The goal is to reduce complexity as much as possible for data scientists to run experiments on EC2 machines or SageMaker with extensions, making workflow efficient. On top of the infrastructure, there aren’t many tools except for neptune.ai, which tracks their experiments.

Check how exactly neptune.ai supports experiment tracking needs.

The team uses a standard technology stack, like libraries for training models, and simple, well-known ways to process datasets quickly and effectively. They combine the libraries, run them on an EC2 machine or SageMaker, and report the experiment metrics on neptune.ai. 

“We focus more on how the scientific process looks than on the extensive tooling. In the future, we may consider improvements to our experimentation process, making it smoother, less bulky, etc. Currently, we’re fine and have built a few solutions to run training jobs on SageMaker or easily run the same code on EC2 machines. ”   — Paweł Pęczek, Machine Learning Engineer at Brainly

They keep their experimentation workflow simple so that their data scientists and researchers don’t have to deal with much engineering work. For them, it works surprisingly well, considering how low the complexity is.

“We also do not want to research our internal model architectures. If there’s a special case, there’s no strict requirement for not doing so. Generally, we use standard architectures from the different areas we work in (speech, text, and vision)—ConvNets and transformer-based architectures.  We are not obsessed with any one type of architecture. We try to experiment and use what works best in specific contexts.”   — Paweł Pęczek, Machine Learning Engineer at Brainly

Model development frameworks and libraries

The computer vision team mostly uses PyTorch for model development, but it’s not always set in stone. If the model development library is good and their team can train and deploy models with it, they could use it.

“We don’t enforce experimentation frameworks for teams. If someone wants to use TensorFlow, they can, and if someone wants to leverage PyTorch, it is also possible. Obviously, within a specific team, there are internal agreements; otherwise, it would be a mess to collaborate daily.”    — Paweł Pęczek, Machine Learning Engineer at Brainly

Deployment infrastructure and tool stack for the visual search team

The team uses standard deployment tools like Flask and other simple solutions and inference servers like TorchServe .

“We use what the Automation Ops provide for us. We take the model and implement a standard solution for serving on EKS. From our perspective, it was just easier, given our existing automation tools.”  — Paweł Pęczek, Machine Learning Engineer at Brainly

On Amazon EKS , they deploy the services using different strategies. In particular, if tests, readiness, and liveness probes are set up correctly, they can avoid deployment if problems come up. They use simple deployment strategies but are looking at other, more complex strategies in the future as the need arises.

Continuous integration and delivery tool stack for the visual search team

“We leverage CI/CD extensively in our workflows for automation and building pipelines. We have a few areas where we extensively leverage the AWS CI/CD Pipeline toolstack.”   — Paweł Pęczek, Machine Learning Engineer at Brainly

The team uses solutions the Automation Ops team has already provided for CI/CD. They can add CI and CD to the experiment code with a few lines of Terraform code. When it comes to pipelines for training, they use the Terraform module to create CI/CD that will initialize the pipelines, test them, and deploy them to SageMaker (Pipelines)  if the tests pass.

They have production and training code bases in GitHub repositories. Each time they modify the code, the definition of the pipeline changes. It rebuilds the Docker image underneath and runs the steps in the pipeline in the defined order. Everything is refreshed, and anyone can run training against a new dataset.

Once the model is approved, the signals from the model registry get intercepted by the CI/CD pipeline, and the model deployment process starts. An integration test runs the holdout data set through the prediction service to see if the metrics match the ones measured during the evaluation stage. 

If the test passes, they’ll know nothing is broken by incorrect input standardization or similar bugs. If everything is fine, they’ll push the service into production.

“We don’t usually try to use extensive third-party solutions if AWS provides something reasonable, especially with the presence of our Automation Ops team that provides the modules we can use.”   — Paweł Pęczek, Machine Learning Engineer at Brainly

Model testing and approval of the CI/CD pipeline

“We test our models after training and verify the metrics, and when it comes to pure engineering, we make sure that everything works end-to-end. We take the test sets or hold-out datasets, push them to the service, and check if the results are the same as previously.” — Paweł Pęczek, Machine Learning Engineer at Brainly

The AI/ML team is responsible for maintaining a healthy set of tests, ensuring that the solution will work as it should. Regarding other teams, they may approach testing ML models differently, especially in tabular ML use cases, by testing on sub-populations of the data.

“It’s a healthy situation when data scientists and ML engineers, in particular, are responsible for delivering tests for the functionalities of their projects. They would not need to rely on anything or anyone else, and there would be no finger-pointing or disagreements. They just need to do the job properly and show others that it works as it should. For us, it would be difficult to achieve complete test standardization across all of the pipelines, but similar pipelines have similar test cases.” — Paweł Pęczek, Machine Learning Engineer at Brainly

The tooling for testing their code is also simple—they use PyTest for unit and integration tests and more sophisticated tests.

“The model approval method depends on the use case. I believe some use cases are so mature that teams can just agree to get automatic approval, which would be after reaching a certain performance threshold.” — Paweł Pęczek, Machine Learning Engineer at Brainly

Most of the time, the user (the machine learning engineer or data scientist) has to keep an eye on the model verification process. To make the process more consistent, they made a maintenance cookbook with clear instructions on what needed to be checked and done to make sure the model met specific quality standards. 

It wouldn’t be enough just to verify the metrics; other qualitative features of the model would also have to be checked. If that is completed and the model is relatively okay, they will push the approval button, and from that moment on, the automated CI/CD pipeline will be triggered.

Managing models and pipelines in production 

Model management is quite context-dependent for different AI/ML teams. For example, when the computer vision team works with image data that requires labeling, managing the model in production will be different from working with tabular data that is processed in another way.

“We try to keep an eye out for any changes in how our services work, how well our models predict, or how the statistics of the data logged in production change. If we detect degradation, we’ll look into the data a little more, and if we find something wrong, we’ll collect and label new datasets.  In the future, we would like to push more of our use cases to MLOps maturity level two (2), where more things related to data and monitoring will be done automatically.” — Paweł Pęczek, Machine Learning Engineer at Brainly

Clients also measure their KPIs, and the team can be notified if something goes wrong.

Model monitoring and governance tools

To get the service performance metrics, the team uses Grafana to observe the model’s statistics and standard logging and monitoring solutions on Amazon Elastic Kubernetes Service (Amazon EKS). They use Prometheus to add statistics about how the services work and make them available as time series. This makes adding new dashboards, monitoring them, and getting alerts easy.

The Automation Ops team provides bundles for monitoring services, which justifies the team’s decision to make their stack as simple as possible to fit into their existing engineering ecosystem. 

“It’s reasonable not to overinvest in different tools if you already have good ones.” — Paweł Pęczek, Machine Learning Engineer at Brainly

In the case of model governance, the team is mainly concerned with GDPR and making sure their data is censored to some degree. For example, they wouldn’t want personal information to get out to labelers or bad content to get out to users. They’d filter and moderate the content as part of their use case.

That’s it! If you want to learn more about Brainly’s technology ecosystem, check out their technology blog .

Thanks to Paweł Pęczek and the team at Brainly for working with us to create this article!

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How To Choose A Research Topic

Step-By-Step Tutorial With Examples + Free Topic Evaluator

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | April 2024

Choosing the right research topic is likely the  most important decision you’ll make on your dissertation or thesis journey. To make the right choice, you need to take a systematic approach and evaluate each of your candidate ideas across a consistent set of criteria. In this tutorial, we’ll unpack five essential criteria that will help you evaluate your prospective research ideas and choose a winner.

Overview: The “Big 5” Key Criteria

• Topic originality or novelty
• Value and significance
• Access to data and equipment
• Time limitations and implications
• Ethical requirements and constraints

Criterion #1: Originality & Novelty

As we’ve discussed extensively on this blog, originality in a research topic is essential. In other words, you need a clear research gap . The uniqueness of your topic determines its contribution to the field and its potential to stand out in the academic community. So, for each of your prospective topics, ask yourself the following questions:

• What research gap and research problem am I filling?
• Does my topic offer new insights?
• Am I combining existing ideas in a unique way?
• Am I taking a unique methodological approach?

To objectively evaluate the originality of each of your topic candidates, rate them on these aspects. This process will not only help in choosing a topic that stands out, but also one that can capture the interest of your audience and possibly contribute significantly to the field of study – which brings us to our next criterion.

Criterion #2: Value & Significance

Next, you’ll need to assess the value and significance of each prospective topic. To do this, you’ll need to ask some hard questions.

• Why is it important to explore these research questions?
• Who stands to benefit from this study?
• How will they benefit, specifically?

By clearly understanding and outlining the significance of each potential topic, you’ll not only be justifying your final choice – you’ll essentially be laying the groundwork for a persuasive research proposal , which is equally important.

Criterion #3: Access to Data & Equipment

Naturally, access to relevant data and equipment is crucial for the success of your research project. So, for each of your prospective topic ideas, you’ll need to evaluate whether you have the necessary resources to collect data and conduct your study.

Here are some questions to ask for each potential topic:

• Will I be able to access the sample of interest (e.g., people, animals, etc.)?
• Do I have (or can I get) access to the required equipment, at the time that I need it?
• Are there costs associated with any of this? If so, what are they?

Keep in mind that getting access to certain types of data may also require special permissions and legalities, especially if your topic involves vulnerable groups (patients, youths, etc.). You may also need to adhere to specific data protection laws, depending on the country. So, be sure to evaluate these aspects thoroughly for each topic. Overlooking any of these can lead to significant complications down the line.

Criterion #4: Time Requirements & Implications

Naturally, having a realistic timeline for each potential research idea is crucial. So, consider the scope of each potential topic and estimate how long each phase of the research will take — from literature review to data collection and analysis, to writing and revisions. Underestimating the time needed for a research project is extremely common , so it’s important to include buffer time for unforeseen delays.

Remember, efficient time management is not just about the duration but also about the timing . For example, if your research involves fieldwork, there may specific times of the year when this is most doable (or not doable at all).  So, be sure to consider both time and timing for each of your prospective topics.

Criterion #5: Ethical Compliance

Failing to adhere to your university’s research ethics policy is a surefire way to get your proposal rejected . So, you’ll need to evaluate each topic for potential ethical issues, especially if your research involves human subjects, sensitive data, or has any potential environmental impact.

Remember that ethical compliance is not just a formality – it’s a responsibility to ensure the integrity and social responsibility of your research. Topics that pose significant ethical challenges are typically the first to be rejected, so you need to take this seriously. It’s also useful to keep in mind that some topics are more “ethically sensitive” than others , which usually means that they’ll require multiple levels of approval. Ideally, you want to avoid this additional admin, so mark down any prospective topics that fall into an ethical “grey zone”.

If you’re unsure about the details of your university’s ethics policy, ask for a copy or speak directly to your course coordinator. Don’t make any assumptions when it comes to research ethics!

Key Takeaways

In this post, we’ve explored how to choose a research topic using a systematic approach. To recap, the “Big 5” assessment criteria include:

• Topic originality and novelty
• Time requirements
• Ethical compliance

Be sure to grab a copy of our free research topic evaluator sheet here to fast-track your topic selection process. If you need hands-on help finding and refining a high-quality research topic for your dissertation or thesis, you can also check out our private coaching service .

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Big Data Projects

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Big Data Projects studies the application of statistical modeling and AI technologies to healthcare.

Mohsen Bayati studies probabilistic and statistical models for decision-making with large-scale and complex data and applies them to healthcare problems. Currently, an area of focus is AI’s use in oncology, and multi-functional research efforts are underway between the GSB and the School of Medicine. For example, AI is the right technology for oncology treatment decision-making methods because of its ability to synthesize rich patient data into prospective individual-level actionable recommendations and retrospectively learn from those decisions at scale.

However, the current set of AI technologies are focused heavily on detection and diagnosis, and major challenges remain in accessing and using the rich set of patient data for the oncologist’s patient-specific treatment decision. The clinical workflow then becomes mainly experience-driven, leading to many care disparities and with many hand-offs between oncology specialists. Dr. Bayati’s research enables developing an oncologist-centric decision support tool to push oncological decision-making and AI research further and in a multidisciplinary way by using AI for day-to-day oncology treatment decisions. He also studies graphical models and message-passing algorithms.

Mohsen Bayati , Faculty Director

Top 15 Big Data Projects (With Source Code)

Introduction, big data project ideas, projects for beginners, intermediate big data projects, advanced projects, big data projects: why are they so important, frequently asked questions, additional resources.

Almost 6,500 million linked gadgets communicate data via the Internet nowadays. This figure will climb to 20,000 million by 2025. This “sea of data” is analyzed by big data to translate it into the information that is reshaping our world. Big data refers to massive data volumes – both organized and unstructured – that bombard enterprises daily. But it’s not simply the type or quantity of data that matters; it’s also what businesses do with it. Big data may be evaluated for insights that help people make better decisions and feel more confident about making key business decisions. Big data refers to vast, diversified amounts of data that are growing at an exponential rate. The volume of data, the velocity or speed with which it is created and collected, and the variety or scope of the data points covered (known as the “three v’s” of big data) are all factors to consider. Big data is frequently derived by data mining and is available in a variety of formats.

Unstructured and structured big data are two types of big data. For large data, the term structured data refers to data that has a set length and format. Numbers, dates, and strings, which are collections of words and numbers, are examples of organized data. Unstructured data is unorganized data that does not fit into a predetermined model or format. It includes information gleaned from social media sources that aid organizations in gathering information on customer demands.

Key Takeaway

Confused about your next job?

• Big data is a large amount of diversified information that is arriving in ever-increasing volumes and at ever-increasing speeds.
• Big data can be structured (typically numerical, readily formatted, to and saved) or unstructured (often non-numerical, difficult to format and store) (more free-form, less quantifiable).
• Big data analysis may benefit nearly every function in a company, but dealing with the clutter and noise can be difficult.
• Big data can be gathered willingly through personal devices and applications, through questionnaires, product purchases, and electronic check-ins, as well as publicly published remarks on social networks and websites.
• Big data is frequently kept in computer databases and examined with software intended to deal with huge, complicated data sets.

Just knowing the theory of big data isn’t going to get you very far. You’ll need to put what you’ve learned into practice. You may put your big data talents to the test by working on big data projects. Projects are an excellent opportunity to put your abilities to the test. They’re also great for your resume. In this article, we are going to discuss some great Big Data projects that you can work on to showcase your big data skills.

1. Traffic control using Big Data

Big Data initiatives that simulate and predict traffic in real-time have a wide range of applications and advantages. The field of real-time traffic simulation has been modeled successfully. However, anticipating route traffic has long been a challenge. This is because developing predictive models for real-time traffic prediction is a difficult endeavor that involves a lot of latency, large amounts of data, and ever-increasing expenses.

The following project is a Lambda Architecture application that monitors the traffic safety and congestion of each street in Chicago. It depicts current traffic collisions, red light, and speed camera infractions, as well as traffic patterns on 1,250 street segments within the city borders.

These datasets have been taken from the City of Chicago’s open data portal:

• Traffic Crashes shows each crash that occurred within city streets as reported in the electronic crash reporting system (E-Crash) at CPD. Citywide data are available starting September 2017.
• Red Light Camera Violations reflect the daily number of red light camera violations recorded by the City of Chicago Red Light Program for each camera since 2014.
• Speed Camera Violations reflect the daily number of speed camera violations recorded by each camera in Children’s Safety Zones since 2014.
• Historical Traffic Congestion Estimates estimates traffic congestion on Chicago’s arterial streets in real-time by monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses.
• Current Traffic Congestion Estimate shows current estimated speed for street segments covering 300 miles of arterial roads. Congestion estimates are produced every ten minutes.

The project implements the three layers of the Lambda Architecture:

• Batch layer – manages the master dataset (the source of truth), which is an immutable, append-only set of raw data. It pre-computes batch views from the master dataset.
• Serving layer – responds to ad-hoc queries by returning pre-computed views (from the batch layer) or building views from the processed data.
• Speed layer – deals with up-to-date data only to compensate for the high latency of the batch layer

Source Code – Traffic Control

2. Search Engine

To comprehend what people are looking for, search engines must deal with trillions of network objects and monitor the online behavior of billions of people. Website material is converted into quantifiable data by search engines. The given project is a full-featured search engine built on top of a 75-gigabyte In this project, we will use several datasets like stopwords.txt (A text file containing all the stop words in the current directory of the code) and wiki_dump.xml (The XML file containing the full data of Wikipedia). Wikipedia corpus with sub-second search latency. The results show wiki pages sorted by TF/IDF (stands for Term Frequency — Inverse Document Frequency) relevance based on the search term/s entered. This project addresses latency, indexing, and huge data concerns with an efficient code and the K-Way merge sort method.

Source Code – Search Engine

3. Medical Insurance Fraud Detection

A unique data science model that uses real-time analysis and classification algorithms to assist predict fraud in the medical insurance market. This instrument can be utilized by the government to benefit patients, pharmacies, and doctors, ultimately assisting in improving industry confidence, addressing rising healthcare expenses, and addressing the impact of fraud. Medical services deception is a major problem that costs Medicare/Medicaid and the insurance business a lot of money.

4 different Big Datasets have been joined in this project to get a single table for final data analysis. The datasets collected are:

• Part D prescriber services- data such as name of doctor, addres of doctor, disease, symptoms etc.
• List of Excluded Individuals and Entities (LEIE) database: This database contains a rundown of people and substances that are prohibited from taking an interest in governmentally financed social insurance programs (for example Medicare) because of past medicinal services extortion. 
• Payments Received by Physician from Pharmaceuticals
• CMS part D dataset- data by Center of Medicare and Medicaid Services

It has been developed by taking consideration of different key features with applying different Machine Learning Algorithms to see which one performs better. The ML algorithms used have been trained to detect any irregularities in the dataset so that the authorities can be alerted.

Source Code – Medical Insurance Fraud

4. Data Warehouse Design for an E-Commerce Site

A data warehouse is essentially a vast collection of data for a company that assists the company in making educated decisions based on data analysis. The data warehouse designed in this project is a central repository for an e-commerce site, containing unified data ranging from searches to purchases made by site visitors. The site can manage supply based on demand (inventory management), logistics, the price for maximum profitability, and advertisements based on searches and things purchased by establishing such a data warehouse. Recommendations can also be made based on tendencies in a certain area, as well as age groups, sex, and other shared interests. This is a data warehouse implementation for an e-commerce website “Infibeam” which sells digital and consumer electronics.

Source Code – Data Warehouse Design

5. Text Mining Project

You will be required to perform text analysis and visualization of the delivered documents as part of this project. For beginners, this is one of the best deep learning project ideas. Text mining is in high demand, and it can help you demonstrate your abilities as a data scientist . You can deploy Natural Language Process Techniques to gain some useful information from the link provided below. The link contains a collection of NLP tools and resources for various languages.

Source Code – Text Mining

6. Big Data Cybersecurity

The major goal of this Big Data project is to use complex multivariate time series data to exploit vulnerability disclosure trends in real-world cybersecurity concerns. This project consists of outlier and anomaly detection technologies based on Hadoop, Spark, and Storm are interwoven with the system’s machine learning and automation engine for real-time fraud detection and intrusion detection to forensics.

For independent Big Data Multi-Inspection / Forensics of high-level risks or volume datasets exceeding local resources, it uses the Ophidia Analytics Framework. Ophidia Analytics Framework is an open-source big data analytics framework that contains cluster-aware parallel operators for data analysis and mining (subsetting, reduction, metadata processing, and so on). The framework is completely connected with Ophidia Server: it takes commands from the server and responds with alerts, allowing processes to run smoothly.

Lumify, an open-source big data analysis, and visualization platform are also included in the Cyber Security System to provide big data analysis and visualization of each instance of fraud or intrusion events into temporary, compartmentalized virtual machines, which creates a full snapshot of the network infrastructure and infected device, allowing for in-depth analytics, forensic review, and providing a transportable threat analysis for Executive level next-steps.

Lumify, a big data analysis and visualization tool developed by Cyberitis is launched using both local and cloud resources (customizable per environment and user). Only the backend servers (Hadoop, Accumulo, Elasticsearch, RabbitMQ, Zookeeper) are included in the Open Source Lumify Dev Virtual Machine. This VM allows developers to get up and running quickly without having to install the entire stack on their development workstations.

Source Code – Big Data Cybersecurity

7. Crime Detection

The following project is a Multi-class classification model for predicting the types of crimes in Toronto city. The developer of the project, using big data ( The dataset collected includes every major crime committed from 2014-2017* in the city of Toronto, with detailed information about the location and time of the offense), has constructed a multi-class classification model using a Random Forest classifier to predict the type of major crime committed based on time of day, neighborhood, division, year, month, etc. using data sourced from Toronto Police.

The use of big data analytics here is to discover crime tendencies automatically. If analysts are given automated, data-driven tools to discover crime patterns, these tools can help police better comprehend crime patterns, allowing for more precise estimates of past crimes and increasing suspicion of suspects.

Source Code – Crime Detection

8. Disease Prediction Based on Symptoms

With the rapid advancement of technology and data, the healthcare domain is one of the most significant study fields in the contemporary era. The enormous amount of patient data is tough to manage. Big Data Analytics makes it easier to manage this information (Electronic Health Records are one of the biggest examples of the application of big data in healthcare). Knowledge derived from big data analysis gives healthcare specialists insights that were not available before. In healthcare, big data is used at every stage of the process, from medical research to patient experience and outcomes. There are numerous ways of treating various ailments throughout the world. Machine Learning and Big Data are new approaches that aid in disease prediction and diagnosis. This research explored how machine learning algorithms can be used to forecast diseases based on symptoms. The following algorithms have been explored in code:

• Naive Bayes
• Decision Tree
• Random Forest
• Gradient Boosting

Source Code – Disease Prediction

9. Yelp Review Analysis

Yelp is a forum for users to submit reviews and rate businesses with a star rating. According to studies, an increase of one star resulted in a 59 percent rise in income for independent businesses. As a result, we believe the Yelp dataset has a lot of potential as a powerful insight source. Customer reviews of Yelp is a gold mine waiting to be discovered.

This project’s main goal is to conduct in-depth analyses of seven different cuisine types of restaurants: Korean, Japanese, Chinese, Vietnamese, Thai, French, and Italian, to determine what makes a good restaurant and what concerns customers, and then make recommendations for future improvement and profit growth. We will mostly evaluate customer evaluations to determine why customers like or dislike the business. We can turn the unstructured data (reviews)  into actionable insights using big data, allowing businesses to better understand how and why customers prefer their products or services and make business improvements as rapidly as feasible.

Source Code – Review Analysis

10. Recommendation System

Thousands, millions, or even billions of objects, such as merchandise, video clips, movies, music, news, articles, blog entries, advertising, and so on, are typically available through online services. The Google Play Store, for example, has millions of apps and YouTube has billions of videos. Netflix Recommendation Engine, their most effective algorithm, is made up of algorithms that select material based on each user profile. Big data provides plenty of user data such as past purchases, browsing history, and comments for Recommendation systems to deliver relevant and effective recommendations. In a nutshell, without massive data, even the most advanced Recommenders will be ineffective. Big data is the driving force behind our mini-movie recommendation system. Over 3,000 titles are filtered at a time by the engine, which uses 1,300 suggestion clusters depending on user preferences. It’s so accurate that customized recommendations from the engine drive 80 percent of Netflix viewer activity. The goal of this project is to compare the performance of various recommendation models on the Hadoop Framework.

Source Code – Recommendation System

11. Anomaly Detection in Cloud Servers

Anomaly detection is a useful tool for cloud platform managers who want to keep track of and analyze cloud behavior in order to improve cloud reliability. It assists cloud platform managers in detecting unexpected system activity so that preventative actions can be taken before a system crash or service failure occurs.

This project provides a reference implementation of a Cloud Dataflow streaming pipeline that integrates with BigQuery ML, Cloud AI Platform to perform anomaly detection. A key component of the implementation leverages Dataflow for feature extraction & real-time outlier identification which has been tested to analyze over 20TB of data.

Source Code – Anomaly Detection

12. Smart Cities Using Big Data

A smart city is a technologically advanced metropolitan region that collects data using various electronic technologies, voice activation methods, and sensors. The information gleaned from the data is utilized to efficiently manage assets, resources, and services; in turn, the data is used to improve operations throughout the city. Data is collected from citizens, devices, buildings, and assets, which is then processed and analyzed to monitor and manage traffic and transportation systems, power plants, utilities, water supply networks, waste, crime detection, information systems, schools, libraries, hospitals, and other community services. Big data obtains this information and with the help of advanced algorithms, smart network infrastructures and various analytics platforms can implement the sophisticated features of a smart city.  This smart city reference pipeline shows how to integrate various media building blocks, with analytics powered by the OpenVINO Toolkit, for traffic or stadium sensing, analytics, and management tasks.

Source Code – Smart Cities

13. Tourist Behavior Analysis

This is one of the most innovative big data project concepts. This Big Data project aims to study visitor behavior to discover travelers’ preferences and most frequented destinations, as well as forecast future tourism demand. 

What is the role of big data in the project? Because visitors utilize the internet and other technologies while on vacation, they leave digital traces that Big Data can readily collect and distribute – the majority of the data comes from external sources such as social media sites. The sheer volume of data is simply too much for a standard database to handle, necessitating the use of big data analytics.  All the information from these sources can be used to help firms in the aviation, hotel, and tourist industries find new customers and advertise their services. It can also assist tourism organizations in visualizing and forecasting current and future trends.

Source Code – Tourist Behavior Analysis

14. Web Server Log Analysis

A web server log keeps track of page requests as well as the actions it has taken. To further examine the data, web servers can be used to store, analyze, and mine the data. Page advertising can be determined and SEO (search engine optimization) can be performed in this manner. Web-server log analysis can be used to get a sense of the overall user experience. This type of processing is advantageous to any company that relies largely on its website for revenue generation or client communication. This interesting big data project demonstrates parsing (including incorrectly formatted strings) and analysis of web server log data.

Source Code – Web Server Log Analysis

15. Image Caption Generator

Because of the rise of social media and the importance of digital marketing, businesses must now upload engaging content. Visuals that are appealing to the eye are essential, but subtitles that describe the images are also required. The usage of hashtags and attention-getting subtitles might help you reach out to the right people even more. Large datasets with correlated photos and captions must be managed. Image processing and deep learning are used to comprehend the image, and artificial intelligence is used to provide captions that are both relevant and appealing. Big Data source code can be written in Python. The creation of image captions isn’t a beginner-level Big Data project proposal and is indeed challenging. The project given below uses a neural network to generate captions for an image using CNN (Convolution Neural Network) and RNN (Recurrent Neural Network) with BEAM Search (Beam search is a heuristic search algorithm that examines a graph by extending the most promising node in a small collection. 

There are currently rich and colorful datasets in the image description generating work, such as MSCOCO, Flickr8k, Flickr30k, PASCAL 1K, AI Challenger Dataset, and STAIR Captions, which are progressively becoming a trend of discussion. The given project utilizes state-of-the-art ML and big data algorithms to build an effective image caption generator.

Source Code – Image Caption Generator

Big Data is a fascinating topic. It helps in the discovery of patterns and outcomes that might otherwise go unnoticed. Big Data is being used by businesses to learn what their customers want, who their best customers are, and why people choose different products. The more information a business has about its customers, the more competitive it is.

It can be combined with Machine Learning to create market strategies based on customer predictions. Companies that use big data become more customer-centric.

This expertise is in high demand and learning it will help you progress your career swiftly. As a result, if you’re new to big data, the greatest thing you can do is brainstorm some big data project ideas. 

We’ve examined some of the best big data project ideas in this article. We began with some simple projects that you can complete quickly. After you’ve completed these beginner tasks, I recommend going back to understand a few additional principles before moving on to the intermediate projects. After you’ve gained confidence, you can go on to more advanced projects.

What are the 3 types of big data? Big data is classified into three main types:

• Unstructured
• Semi-structured

What can big data be used for? Some important use cases of big data are:

• Improving Science and research
• Improving governance
• Smart cities
• Understanding and targeting customers
• Understanding and Optimizing Business Processes
• Improving Healthcare and Public Health
• Financial Trading
• Optimizing Machine and Device Performance

What industries use big data? Big data finds its application in various domains. Some fields where big data can be used efficiently are:

• Travel and tourism
• Financial and banking sector
• Telecommunication and media
• Banking Sector
• Government and Military
• Social Media
• Big Data Tools
• Big Data Engineer
• Applications of Big Data
• Big Data Interview Questions
• Big Data Projects

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How Brainly’s Data & Analytics Department Uses Data for Decision-Making

The modern student looks to various sources for knowledge - but one of the best ways to learn is through a conversation with an educator. That’s what Brainly is about - connecting those who are learning with those who teach.

At Brainly, data is the key to decision-making, for both internal matters and determining how to best help their users. Understanding the roles related to Data Analysis, Data Science, Data Engineering, or Data Governance helps us to better understand how Brainly uses data and why it’s crucial. We interviewed three Brainly data experts from the Data & Analytics Department: Ewa Bugajska, Senior Lead Data Analyst in the Analytics Center of Excellence, Katarzyna Bodzioch-Marczewska, Solutions Architect in Data Governance, and Tomasz Sienkiewicz, the Director of the Data & Analytics Department.

What can you tell me about your Data organization - what makes it so unique?

[TS] We see that companies have different approaches to how the Data or Analytics teams operate - quite often it’s either a centralized or a decentralized model. In a centralized model there’s one Data team that receives requests from the whole organization; in the decentralized model all Data experts are embedded in the business units they’re supporting and they don’t work together.

What makes Brainly unique is our hybrid organizational model in terms of how we use data and how Data Analysts work. On one hand, they’re embedded in teams, working with products, marketing, and more. At the same time, we have a central team to ensure that all Data Analysts are able to cooperate and share knowledge with each other, work according to similar standards, and are hired and onboarded using a standardized process.

How are Data teams set up at Brainly?

[TS] Basically, our data teams are divided into 3 main areas. The Data Analytics team helps stakeholders make data-driven decisions. Data Analysts analyze data, draw conclusions, find opportunities, and based on these, stakeholders make decisions.

Our AI/ML team uses data to build ML-based products, mostly focusing on how to use data to build solutions for end-users. And Brainly’s Data Engineers make sure that data is collected, transformed, and stored properly.

Can you tell us a bit more about how the central Data Analytics Department is divided?

[TS] There are 2 areas - the Analytics “Center of Excellence” and Data Governance.

[EB] My area is the Analytics Center of Excellence, where we make sure to hire the best data analytics talent, in cooperation with our Talent Acquisition team, and provide them with an onboarding experience that prepares them for working with our data and systems. I support Data Analysts’ professional growth, define career paths, organize opportunities for the analysts to share and acquire knowledge, as well as lead initiatives, focused on standardizing some of the work that all analysts do, regardless of the team they work in.

[KB] My area, Data Governance, is about controlling some aspects of our data management system. I build policies and processes to make sure our data quality meets our standards and our data is safe but accessible for everyone who needs it. Right now, my main focus is the data catalog that we’re building with Data Analysts and Data Engineers.

Why is data so important to Brainly and how do users benefit from it? 

[TS] The way I see it, when you run a business, you make decisions. The higher in the company hierarchy, the more important the decisions - they’re made in different ways, based on your gut, past experiences, and biases. Most importantly, you make decisions based on insights and data.

Generally speaking, companies that use data to make decisions tend to be more successful. This is how we act at Brainly - we use data to make big decisions so we can grow and build quality products as quickly as possible.

What does Brainly do to support their “Data Culture”?

[TS] We support access to and the understanding of data. For example, Kasia’s project increases accessibility, and Ewa’s A/B testing project helps us understand that data.

[KB] We support the growth of the Data Community. One way of doing that is gathering tribal knowledge about data and building a data catalog. The data catalog tool that we’re currently onboarding will help everybody discover data faster, understand it and collaborate around it. It will be a trustworthy and easily accessible source of information about the data for everyone in Brainly.

[EB] One of the projects that we’re currently running in partnership with the Product division is focused on improving our approach to running A/B tests within the whole company through building a common framework, proper education, organization, and a standardized approach to reporting. Thanks to that, our Analysts can be more efficient when summarizing the A/B test results, and business stakeholders understand the outcomes so they can make data-driven decisions faster.

Can you describe Brainly’s company culture and how you work?

[EB] Brainly is a company with a great mission and amazing people who help to realize it. You rarely have the privilege to work with people who care about each other and the company’s mission and who are so open to learning and sharing knowledge. 

Since we’re an education company, it may sound obvious that learning is a big deal for us, but any initiative that is focused on sharing knowledge is well received. We learn and grow together, in more than just our own area of expertise.

Do you feel the Brainly value “Stay Curious: Always wonder. Always explore” represents your work at Brainly?

[TS] Absolutely. I like to say, “Win or learn” instead of “Win or Lose”!

[EB] Even though we aren’t a product team, we still have regular retrospective meetings to identify if there are issues we should address right away, and we also actively ask for feedback within the processes we participate in.

‍ [KB] We talk about technical problems and review our solutions. Nothing gets swept under the rug. Our team members are open to doing it because they feel they are in a safe space. It’s always a great opportunity to share experiences and learn from each other.

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25+ Solved End-to-End Big Data Projects with Source Code

Solved End-to-End Real World Mini Big Data Projects Ideas with Source Code For Beginners and Students to master big data tools like Hadoop and Spark.

Ace your big data analytics interview by adding some unique and exciting Big Data projects to your portfolio. This blog lists over 20 big data analytics projects you can work on to showcase your big data skills and gain hands-on experience in big data tools and technologies. You will find several big data projects depending on your level of expertise- big data projects for students, big data projects for beginners, etc.

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Have you ever looked for sneakers on Amazon and seen advertisements for similar sneakers while searching the internet for the perfect cake recipe? Maybe you started using Instagram to search for some fitness videos, and now, Instagram keeps recommending videos from fitness influencers to you. And even if you’re not very active on social media, I’m sure you now and then check your phone before leaving the house to see what the traffic is like on your route to know how long it could take you to reach your destination. None of this would have been possible without the application of big data analysis process on by the modern data driven companies. We bring the top big data projects for 2023 that are specially curated for students, beginners, and anybody looking to get started with mastering data skills.

Table of Contents

What is a big data project, how do you create a good big data project, 25+ big data project ideas to help boost your resume , big data project ideas for beginners, intermediate projects on data analytics, advanced level examples of big data projects, real-time big data projects with source code, sample big data project ideas for final year students, big data project ideas using hadoop , big data projects using spark, gcp and aws big data projects, best big data project ideas for masters students, fun big data project ideas, top 5 apache big data projects, top big data projects on github with source code, level-up your big data expertise with projectpro's big data projects, faqs on big data projects.

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on structured and unstructured data for several purposes, including predictive modeling and other advanced analytics applications. Before actually working on any big data projects, data engineers must acquire proficient knowledge in the relevant areas, such as deep learning, machine learning, data visualization , data analytics, data science, etc. 

Many platforms, like GitHub and ProjectPro, offer various big data projects for professionals at all skill levels- beginner, intermediate, and advanced. However, before moving on to a list of big data project ideas worth exploring and adding to your portfolio, let us first get a clear picture of what big data is and why everyone is interested in it.

Kicking off a big data analytics project is always the most challenging part. You always encounter questions like what are the project goals, how can you become familiar with the dataset, what challenges are you trying to address,  what are the necessary skills for this project, what metrics will you use to evaluate your model, etc.

Well! The first crucial step to launching your project initiative is to have a solid project plan. To build a big data project, you should always adhere to a clearly defined workflow. Before starting any big data project, it is essential to become familiar with the fundamental processes and steps involved, from gathering raw data to creating a machine learning model to its effective implementation.

Understand the Business Goals of the Big Data Project

The first step of any good big data analytics project is understanding the business or industry that you are working on. Go out and speak with the individuals whose processes you aim to transform with data before you even consider analyzing the data. Establish a timeline and specific key performance indicators afterward. Although planning and procedures can appear tedious, they are a crucial step to launching your data initiative! A definite purpose of what you want to do with data must be identified, such as a specific question to be answered, a data product to be built, etc., to provide motivation, direction, and purpose.

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Collect Data for the Big Data Project

The next step in a big data project is looking for data once you've established your goal. To create a successful data project, collect and integrate data from as many different sources as possible. 

Here are some options for collecting data that you can utilize:

Connect to an existing database that is already public or access your private database.

Consider the APIs for all the tools your organization has been utilizing and the data they have gathered. You must put in some effort to set up those APIs so that you can use the email open and click statistics, the support request someone sent, etc.

There are plenty of datasets on the Internet that can provide more information than what you already have. There are open data platforms in several regions (like data.gov in the U.S.). These open data sets are a fantastic resource if you're working on a personal project for fun.

Data Preparation and Cleaning

The data preparation step, which may consume up to 80% of the time allocated to any big data or data engineering project, comes next. Once you have the data, it's time to start using it. Start exploring what you have and how you can combine everything to meet the primary goal. To understand the relevance of all your data, start making notes on your initial analyses and ask significant questions to businesspeople, the IT team, or other groups. Data Cleaning is the next step. To ensure that data is consistent and accurate, you must review each column and check for errors, missing data values, etc.

Making sure that your project and your data are compatible with data privacy standards is a key aspect of data preparation that should not be overlooked. Personal data privacy and protection are becoming increasingly crucial, and you should prioritize them immediately as you embark on your big data journey. You must consolidate all your data initiatives, sources, and datasets into one location or platform to facilitate governance and carry out privacy-compliant projects. 

New Projects

Data Transformation and Manipulation

Now that the data is clean, it's time to modify it so you can extract useful information. Starting with combining all of your various sources and group logs will help you focus your data on the most significant aspects. You can do this, for instance, by adding time-based attributes to your data, like:

Acquiring date-related elements (month, hour, day of the week, week of the year, etc.)

Calculating the variations between date-column values, etc.

Joining datasets is another way to improve data, which entails extracting columns from one dataset or tab and adding them to a reference dataset. This is a crucial component of any analysis, but it can become a challenge when you have many data sources.

 Visualize Your Data

Now that you have a decent dataset (or perhaps several), it would be wise to begin analyzing it by creating beautiful dashboards, charts, or graphs. The next stage of any data analytics project should focus on visualization because it is the most excellent approach to analyzing and showcasing insights when working with massive amounts of data.

Another method for enhancing your dataset and creating more intriguing features is to use graphs. For instance, by plotting your data points on a map, you can discover that some geographic regions are more informative than some other nations or cities.

Build Predictive Models Using Machine Learning Algorithms

Machine learning algorithms can help you take your big data project to the next level by providing you with more details and making predictions about future trends. You can create models to find trends in the data that were not visible in graphs by working with clustering techniques (also known as unsupervised learning). These organize relevant outcomes into clusters and more or less explicitly state the characteristic that determines these outcomes.

Advanced data scientists can use supervised algorithms to predict future trends. They discover features that have influenced previous data patterns by reviewing historical data and can then generate predictions using these features. 

Lastly, your predictive model needs to be operationalized for the project to be truly valuable. Deploying a machine learning model for adoption by all individuals within an organization is referred to as operationalization.

Repeat The Process

This is the last step in completing your big data project, and it's crucial to the whole data life cycle. One of the biggest mistakes individuals make when it comes to machine learning is assuming that once a model is created and implemented, it will always function normally. On the contrary, if models aren't updated with the latest data and regularly modified, their quality will deteriorate with time.

You need to accept that your model will never indeed be "complete" to accomplish your first data project effectively. You need to continually reevaluate, retrain it, and create new features for it to stay accurate and valuable. 

If you are a newbie to Big Data, keep in mind that it is not an easy field, but at the same time, remember that nothing good in life comes easy; you have to work for it. The most helpful way of learning a skill is with some hands-on experience. Below is a list of Big Data analytics project ideas and an idea of the approach you could take to develop them; hoping that this could help you learn more about Big Data and even kick-start a career in Big Data. 

Yelp Data Processing Using Spark And Hive Part 1

Yelp Data Processing using Spark and Hive Part 2

Hadoop Project for Beginners-SQL Analytics with Hive

Tough engineering choices with large datasets in Hive Part - 1

Finding Unique URL's using Hadoop Hive

AWS Project - Build an ETL Data Pipeline on AWS EMR Cluster

Orchestrate Redshift ETL using AWS Glue and Step Functions

Analyze Yelp Dataset with Spark & Parquet Format on Azure Databricks

Data Warehouse Design for E-commerce Environments

Analyzing Big Data with Twitter Sentiments using Spark Streaming

PySpark Tutorial - Learn to use Apache Spark with Python

Tough engineering choices with large datasets in Hive Part - 2

Event Data Analysis using AWS ELK Stack

Web Server Log Processing using Hadoop

Data processing with Spark SQL

Build a Time Series Analysis Dashboard with Spark and Grafana

GCP Data Ingestion with SQL using Google Cloud Dataflow

Deploying auto-reply Twitter handle with Kafka, Spark, and LSTM

Dealing with Slowly Changing Dimensions using Snowflake

Spark Project -Real-Time data collection and Spark Streaming Aggregation

Snowflake Real-Time Data Warehouse Project for Beginners-1

Real-Time Log Processing using Spark Streaming Architecture

Real-Time Auto Tracking with Spark-Redis

Building Real-Time AWS Log Analytics Solution

Explore real-world Apache Hadoop projects by ProjectPro and land your Big Data dream job today!

In this section, you will find a list of good big data project ideas for masters students.

Hadoop Project-Analysis of Yelp Dataset using Hadoop Hive

Online Hadoop Projects -Solving small file problem in Hadoop

Airline Dataset Analysis using Hadoop, Hive, Pig, and Impala

AWS Project-Website Monitoring using AWS Lambda and Aurora

Explore features of Spark SQL in practice on Spark 2.0

MovieLens Dataset Exploratory Analysis

Bitcoin Data Mining on AWS

Create A Data Pipeline Based On Messaging Using PySpark And Hive - Covid-19 Analysis

Spark Project-Analysis and Visualization on Yelp Dataset

Project Ideas on Big Data Analytics

Let us now begin with a more detailed list of good big data project ideas that you can easily implement.

This section will introduce you to a list of project ideas on big data that use Hadoop along with descriptions of how to implement them.

1. Visualizing Wikipedia Trends

Human brains tend to process visual data better than data in any other format. 90% of the information transmitted to the brain is visual, and the human brain can process an image in just 13 milliseconds. Wikipedia is a page that is accessed by people all around the world for research purposes, general information, and just to satisfy their occasional curiosity. 

Raw page data counts from Wikipedia can be collected and processed via Hadoop. The processed data can then be visualized using Zeppelin notebooks to analyze trends that can be supported based on demographics or parameters. This is a good pick for someone looking to understand how big data analysis and visualization can be achieved through Big Data and also an excellent pick for an Apache Big Data project idea. 

Visualizing Wikipedia Trends Big Data Project with Source Code .

2. Visualizing Website Clickstream Data

Clickstream data analysis refers to collecting, processing, and understanding all the web pages a particular user visits. This analysis benefits web page marketing, product management, and targeted advertisement. Since users tend to visit sites based on their requirements and interests, clickstream analysis can help to get an idea of what a user is looking for. 

Visualization of the same helps in identifying these trends. In such a manner, advertisements can be generated specific to individuals. Ads on webpages provide a source of income for the webpage, and help the business publishing the ad reach the customer and at the same time, other internet users. This can be classified as a Big Data Apache project by using Hadoop to build it.

Big Data Analytics Projects Solution for Visualization of Clickstream Data on a Website

3. Web Server Log Processing

A web server log maintains a list of page requests and activities it has performed. Storing, processing, and mining the data on web servers can be done to analyze the data further. In this manner, webpage ads can be determined, and SEO (Search engine optimization) can also be done. A general overall user experience can be achieved through web-server log analysis. This kind of processing benefits any business that heavily relies on its website for revenue generation or to reach out to its customers. The Apache Hadoop open source big data project ecosystem with tools such as Pig, Impala, Hive, Spark, Kafka Oozie, and HDFS can be used for storage and processing.

Big Data Project using Hadoop with Source Code for Web Server Log Processing 

This section will provide you with a list of projects that utilize Apache Spark for their implementation.

4. Analysis of Twitter Sentiments Using Spark Streaming

Sentimental analysis is another interesting big data project topic that deals with the process of determining whether a given opinion is positive, negative, or neutral. For a business, knowing the sentiments or the reaction of a group of people to a new product launch or a new event can help determine the profitability of the product and can help the business to have a more extensive reach by getting an idea of the feel of the customers. From a political standpoint, the sentiments of the crowd toward a candidate or some decision taken by a party can help determine what keeps a specific group of people happy and satisfied. You can use Twitter sentiments to predict election results as well. 

Sentiment analysis has to be done for a large dataset since there are over 180 million monetizable daily active users ( https://www.businessofapps.com/data/twitter-statistics/) on Twitter. The analysis also has to be done in real-time. Spark Streaming can be used to gather data from Twitter in real time. NLP (Natural Language Processing) models will have to be used for sentimental analysis, and the models will have to be trained with some prior datasets. Sentiment analysis is one of the more advanced projects that showcase the use of Big Data due to its involvement in NLP.

Access Big Data Project Solution to Twitter Sentiment Analysis

5. Real-time Analysis of Log-entries from Applications Using Streaming Architectures

If you are looking to practice and get your hands dirty with a real-time big data project, then this big data project title must be on your list. Where web server log processing would require data to be processed in batches, applications that stream data will have log files that would have to be processed in real-time for better analysis. Real-time streaming behavior analysis gives more insight into customer behavior and can help find more content to keep the users engaged. Real-time analysis can also help to detect a security breach and take necessary action immediately. Many social media networks work using the concept of real-time analysis of the content streamed by users on their applications. Spark has a Streaming tool that can process real-time streaming data.

Access Big Data Spark Project Solution to Real-time Analysis of log-entries from applications using Streaming Architecture

6. Analysis of Crime Datasets

Analysis of crimes such as shootings, robberies, and murders can result in finding trends that can be used to keep the police alert for the likelihood of crimes that can happen in a given area. These trends can help to come up with a more strategized and optimal planning approach to selecting police stations and stationing personnel. 

With access to CCTV surveillance in real-time, behavior detection can help identify suspicious activities. Similarly, facial recognition software can play a bigger role in identifying criminals. A basic analysis of a crime dataset is one of the ideal Big Data projects for students. However, it can be made more complex by adding in the prediction of crime and facial recognition in places where it is required.

Big Data Analytics Projects for Students on Chicago Crime Data Analysis with Source Code

Explore Categories

In this section, you will find big data projects that rely on cloud service providers such as AWS and GCP.

7. Build a Scalable Event-Based GCP Data Pipeline using DataFlow

Suppose you are running an eCommerce website, and a customer places an order. In that case, you must inform the warehouse team to check the stock availability and commit to fulfilling the order. After that, the parcel has to be assigned to a delivery firm so it can be shipped to the customer. For such scenarios, data-driven integration becomes less comfortable, so you must prefer event-based data integration.

This project will teach you how to design and implement an event-based data integration pipeline on the Google Cloud Platform by processing data using DataFlow .

Data Description: You will use the Covid-19 dataset(COVID-19 Cases.csv) from data.world , for this project, which contains a few of the following attributes:

people_positive_cases_count

county_name

data_source

Language Used: Python 3.7

Services: Cloud Composer , Google Cloud Storage (GCS), Pub-Sub , Cloud Functions, BigQuery, BigTable

Big Data Project with Source Code: Build a Scalable Event-Based GCP Data Pipeline using DataFlow  

8. Topic Modeling

The future is AI! You must have come across similar quotes about artificial intelligence (AI). Initially, most people found it difficult to believe that could be true. Still, we are witnessing top multinational companies drift towards automating tasks using machine learning tools. 

Understand the reason behind this drift by working on one of our repository's most practical data engineering project examples .

Project Objective: Understand the end-to-end implementation of Machine learning operations (MLOps) by using cloud computing .

Learnings from the Project: This project will introduce you to various applications of AWS services . You will learn how to convert an ML application to a Flask Application and its deployment using Gunicord webserver. You will be implementing this project solution in Code Build. This project will help you understand ECS Cluster Task Definition.

Tech Stack:

Language: Python

Libraries: Flask, gunicorn, scipy , nltk , tqdm, numpy, joblib, pandas, scikit_learn, boto3

Services: Flask, Docker, AWS, Gunicorn

Source Code: MLOps AWS Project on Topic Modeling using Gunicorn Flask

9. MLOps on GCP Project for Autoregression using uWSGI Flask

Here is a project that combines Machine Learning Operations (MLOps) and Google Cloud Platform (GCP). As companies are switching to automation using machine learning algorithms, they have realized hardware plays a crucial role. Thus, many cloud service providers have come up to help such companies overcome their hardware limitations. Therefore, we have added this project to our repository to assist you with the end-to-end deployment of a machine learning project .

Project Objective: Deploying the moving average time-series machine-learning model on the cloud using GCP and Flask.

Learnings from the Project: You will work with Flask and uWSGI model files in this project. You will learn about creating Docker Images and Kubernetes architecture. You will also get to explore different components of GCP and their significance. You will understand how to clone the git repository with the source repository. Flask and Kubernetes deployment will also be discussed in this project.

Tech Stack: Language - Python

Services - GCP, uWSGI, Flask, Kubernetes, Docker

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This section has good big data project ideas for graduate students who have enrolled in a master course.

10. Real-time Traffic Analysis

Traffic is an issue in many major cities, especially during some busier hours of the day. If traffic is monitored in real-time over popular and alternate routes, steps could be taken to reduce congestion on some roads. Real-time traffic analysis can also program traffic lights at junctions – stay green for a longer time on higher movement roads and less time for roads showing less vehicular movement at a given time. Real-time traffic analysis can help businesses manage their logistics and plan their commute accordingly for working-class individuals. Concepts of deep learning can be used to analyze this dataset properly.

11. Health Status Prediction

“Health is wealth” is a prevalent saying. And rightly so, there cannot be wealth unless one is healthy enough to enjoy worldly pleasures. Many diseases have risk factors that can be genetic, environmental, dietary, and more common for a specific age group or sex and more commonly seen in some races or areas. By gathering datasets of this information relevant for particular diseases, e.g., breast cancer, Parkinson’s disease, and diabetes, the presence of more risk factors can be used to measure the probability of the onset of one of these issues. 

In cases where the risk factors are not already known, analysis of the datasets can be used to identify patterns of risk factors and hence predict the likelihood of onset accordingly. The level of complexity could vary depending on the type of analysis that has to be done for different diseases. Nevertheless, since prediction tools have to be applied, this is not a beginner-level big data project idea.

12. Analysis of Tourist Behavior

Tourism is a large sector that provides a livelihood for several people and can adversely impact a country's economy.. Not all tourists behave similarly simply because individuals have different preferences. Analyzing this behavior based on decision-making, perception, choice of destination, and level of satisfaction can be used to help travelers and locals have a more wholesome experience. Behavior analysis, like sentiment analysis, is one of the more advanced project ideas in the Big Data field.

13. Detection of Fake News on Social Media

With the popularity of social media, a major concern is the spread of fake news on various sites. Even worse, this misinformation tends to spread even faster than factual information. According to Wikipedia, fake news can be visual-based, which refers to images, videos, and even graphical representations of data, or linguistics-based, which refers to fake news in the form of text or a string of characters. Different cues are used based on the type of news to differentiate fake news from real. A site like Twitter has 330 million users , while Facebook has 2.8 billion users. A large amount of data will make rounds on these sites, which must be processed to determine the post's validity. Various data models based on machine learning techniques and computational methods based on NLP will have to be used to build an algorithm that can be used to detect fake news on social media.

Access Solution to Interesting Big Data Project on Detection of Fake News

14. Prediction of Calamities in a Given Area

Certain calamities, such as landslides and wildfires, occur more frequently during a particular season and in certain areas. Using certain geospatial technologies such as remote sensing and GIS (Geographic Information System) models makes it possible to monitor areas prone to these calamities and identify triggers that lead to such issues. 

If calamities can be predicted more accurately, steps can be taken to protect the residents from them, contain the disasters, and maybe even prevent them in the first place. Past data of landslides has to be analyzed, while at the same time, in-site ground monitoring of data has to be done using remote sensing. The sooner the calamity can be identified, the easier it is to contain the harm. The need for knowledge and application of GIS adds to the complexity of this Big Data project.

15. Generating Image Captions

With the emergence of social media and the importance of digital marketing, it has become essential for businesses to upload engaging content. Catchy images are a requirement, but captions for images have to be added to describe them. The additional use of hashtags and attention-drawing captions can help a little more to reach the correct target audience. Large datasets have to be handled which correlate images and captions. 

This involves image processing and deep learning to understand the image and artificial intelligence to generate relevant but appealing captions. Python can be used as the Big Data source code. Image caption generation cannot exactly be considered a beginner-level Big Data project idea. It is probably better to get some exposure to one of the projects before proceeding with this.

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16. Credit Card Fraud Detection

The goal is to identify fraudulent credit card transactions, so a customer is not billed for an item that the customer did not purchase. This can tend to be challenging since there are huge datasets, and detection has to be done as soon as possible so that the fraudsters do not continue to purchase more items. Another challenge here is the data availability since the data is supposed to be primarily private. Since this project involves machine learning, the results will be more accurate with a larger dataset. Data availability can pose a challenge in this manner. Credit card fraud detection is helpful for a business since customers are likely to trust companies with better fraud detection applications, as they will not be billed for purchases made by someone else. Fraud detection can be considered one of the most common Big Data project ideas for beginners and students.

If you are looking for big data project examples that are fun to implement then do not miss out on this section.

17. GIS Analytics for Better Waste Management

Due to urbanization and population growth, large amounts of waste are being generated globally. Improper waste management is a hazard not only to the environment but also to us. Waste management involves the process of handling, transporting, storing, collecting, recycling, and disposing of the waste generated. Optimal routing of solid waste collection trucks can be done using GIS modeling to ensure that waste is picked up, transferred to a transfer site, and reaches the landfills or recycling plants most efficiently. GIS modeling can also be used to select the best sites for landfills. The location and placement of garbage bins within city localities must also be analyzed. 

18. Customized Programs for Students

We all tend to have different strengths and paces of learning. There are different kinds of intelligence, and the curriculum only focuses on a few things. Data analytics can help modify academic programs to nurture students better. Programs can be designed based on a student’s attention span and can be modified according to an individual’s pace, which can be different for different subjects. E.g., one student may find it easier to grasp language subjects but struggle with mathematical concepts.

In contrast, another might find it easier to work with math but not be able to breeze through language subjects. Customized programs can boost students’ morale, which could also reduce the number of dropouts. Analysis of a student’s strong subjects, monitoring their attention span, and their responses to specific topics in a subject can help build the dataset to create these customized programs.

19. Real-time Tracking of Vehicles

Transportation plays a significant role in many activities. Every day, goods have to be shipped across cities and countries; kids commute to school, and employees have to get to work. Some of these modes might have to be closely monitored for safety and tracking purposes. I’m sure parents would love to know if their children’s school buses were delayed while coming back from school for some reason. 

Taxi applications have to keep track of their users to ensure the safety of the drivers and the users. Tracking has to be done in real-time, as the vehicles will be continuously on the move. Hence, there will be a continuous stream of data flowing in. This data has to be processed, so there is data available on how the vehicles move so that improvements in routes can be made if required but also just for information on the general whereabouts of the vehicle movement.

20. Analysis of Network Traffic and Call Data Records

There are large chunks of data-making rounds in the telecommunications industry. However, very little of this data is currently being used to improve the business. According to a MindCommerce study: “An average telecom operator generates billions of records per day, and data should be analyzed in real or near real-time to gain maximum benefit.” 

The main challenge here is that these large amounts of data must be processed in real-time. With big data analysis, telecom industries can make decisions that can improve the customer experience by monitoring the network traffic. Issues such as call drops and network interruptions must be closely monitored to be addressed accordingly. By evaluating the usage patterns of customers, better service plans can be designed to meet these required usage needs. The complexity and tools used could vary based on the usage requirements of this project.

This section contains project ideas in big data that are primarily open-source and have been developed by Apache.

Apache Hadoop is an open-source big data processing framework that allows distributed storage and processing of large datasets across clusters of commodity hardware. It provides a scalable, reliable, and cost-effective solution for processing and analyzing big data.

22. Apache Spark

Apache Spark is an open-source big data processing engine that provides high-speed data processing capabilities for large-scale data processing tasks. It offers a unified analytics platform for batch processing, real-time processing, machine learning, and graph processing.

23. Apache Nifi 

Apache NiFi is an open-source data integration tool that enables users to easily and securely transfer data between systems, databases, and applications. It provides a web-based user interface for creating, scheduling, and monitoring data flows, making it easy to manage and automate data integration tasks.

24. Apache Flink

Apache Flink is an open-source big data processing framework that provides scalable, high-throughput, and fault-tolerant data stream processing capabilities. It offers low-latency data processing and provides APIs for batch processing, stream processing, and graph processing.

25. Apache Storm

Apache Storm is an open-source distributed real-time processing system that provides scalable and fault-tolerant stream processing capabilities. It allows users to process large amounts of data in real-time and provides APIs for creating data pipelines and processing data streams.

Does Big Data sound difficult to work with? Work on end-to-end solved Big Data Projects using Spark , and you will know how easy it is!

This section has projects on big data along with links of their source code on GitHub.

26. Fruit Image Classification

This project aims to make a mobile application to enable users to take pictures of fruits and get details about them for fruit harvesting. The project develops a data processing chain in a big data environment using Amazon Web Services (AWS) cloud tools, including steps like dimensionality reduction and data preprocessing and implements a fruit image classification engine. 

The project involves generating PySpark scripts and utilizing the AWS cloud to benefit from a Big Data architecture (EC2, S3, IAM) built on an EC2 Linux server. This project also uses DataBricks since it is compatible with AWS.

Source Code: Fruit Image Classification

27. Airline Customer Service App

In this project, you will build a web application that uses machine learning and Azure data bricks to forecast travel delays using weather data and airline delay statistics. Planning a bulk data import operation is the first step in the project. Next comes preparation, which includes cleaning and preparing the data for testing and building your machine learning model. 

This project will teach you how to deploy the trained model to Docker containers for on-demand predictions after storing it in Azure Machine Learning Model Management. It transfers data using Azure Data Factory (ADF) and summarises data using Azure Databricks and Spark SQL . The project uses Power BI to visualize batch forecasts.

Source Code: Airline Customer Service App

28. Criminal Network Analysis

This fascinating big data project seeks to find patterns to predict and detect links in a dynamic criminal network. This project uses a stream processing technique to extract relevant information as soon as data is generated since the criminal network is a dynamic social graph. It also suggests three brand-new social network similarity metrics for criminal link discovery and prediction. The next step is to develop a flexible data stream processing application using the Apache Flink framework, which enables the deployment and evaluation of the newly proposed and existing metrics.

Source Code- Criminal Network Analysis

Trying out these big data project ideas mentioned above in this blog will help you get used to the popular tools in the industry. But these projects are not enough if you are planning to land a job in the big data industry. And if you are curious about what else will get you closer to landing your dream job, then we highly recommend you check out ProjectPro . ProjectPro hosts a repository of solved projects in Data Science and Big Data prepared by experts in the industry. It offers a subscription to that repository that contains solutions in the form of guided videos along with supporting documentation to help you understand the projects end-to-end. So, don’t wait more to get your hands dirty with ProjectPro projects and subscribe to the repository today!

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1. Why are big data projects important?

Big data projects are important as they will help you to master the necessary big data skills for any job role in the relevant field. These days, most businesses use big data to understand what their customers want, their best customers, and why individuals select specific items. This indicates a huge demand for big data experts in every industry, and you must add some good big data projects to your portfolio to stay ahead of your competitors.

2. What are some good big data projects?

Design a Network Crawler by Mining Github Social Profiles. In this big data project, you'll work on a Spark GraphX Algorithm and a Network Crawler to mine the people relationships around various Github projects.

Visualize Daily Wikipedia Trends using Hadoop - You'll build a Spark GraphX Algorithm and a Network Crawler to mine the people relationships around various Github projects. 

Modeling & Thinking in Graphs(Neo4J) using Movielens Dataset - You will reconstruct the movielens dataset in a graph structure and use that structure to answer queries in various ways in this Neo4j big data project.

3. How long does it take to complete a big data project?

A big data project might take a few hours to hundreds of days to complete. It depends on various factors such as the type of data you are using, its size, where it's stored, whether it is easily accessible, whether you need to perform any considerable amount of ETL processing on the data, etc. 

About the Author

ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

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Use Data to Revolutionize Project Planning

• Yael Grushka-Cockayne

Overcome your inclination to overpromise and under-deliver.

Planning projects accurately is notoriously difficult. According to the 2018 “Pulse of the Profession” study conducted by the Project Management Institute, between 2011 to 2018 only 50% of projects where completed on time and 55% were within budget. Even though firms have been investing in project management techniques since the 1970s, the accuracy of their project plans have not improved much. Inaccurate forecasts of project durations, costs, resources, and benefits are a major source of risk and can affect leaders’ careers, as well as organizations’ growth opportunities and the health of the economy at large. But today, data-driven prediction and decision-making offer unprecedented opportunities in the field of project planning. Using historical data on projects’ initial forecasted completion dates and total costs, among other measures, accuracy estimates can be established. Such accuracy estimates can then be used when forecasting and setting new projects’ goals.

The California bullet train between San Diego and San Francisco. Lockheed Martin’s Joint Strike Fighter program. Berlin’s Brandenburg Airport. Apple’s AirPower wireless charging pad. These are just a few examples of projects that suffered severe schedule delays and cost overruns, or that were unable to deliver on their promised scope.

• YG Yael Grushka-Cockayne is an associate professor of Technology and Operations Management at Harvard Business School, where her research and teaching activities focus on data science, forecasting, project management, and behavioral decision-making. In 2014, Yael was named one of “21 Thought-Leader Professors” in Data Science.

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How to Develop Homework Help App Like Brainly?

Updated on Dec 18th, 2023

With the advancement of new technologies and trends, it is undeniable that traditional learning methods are being replaced by e-learning and homework-help apps. Mobile apps are a great example of technological advancements that assist students in making learning more exciting and rewarding.

Many individuals are interested in studying on the go, and mobile solutions are becoming more popular. According to Statista , over 43% of US students use mobile applications for homework, and 80% believe that using mobile educational apps would help them enhance their knowledge.

Brainly has emerged to be one of the most popular homework apps, offering insights and other study materials to everyone, including students, instructors, and parents.

As a elearning app development company , we have prepared this article for anyone who wants to run an educational business or is looking to start one by launching an educational app.

In this blog, we’ll discuss one of the most prevalent categories of educational apps: homework-help apps for students.

We’ll also discuss how to develop a homework-help app like Brainly.

• Brainly’s approach is based on collaborative learning, where students can ask questions, get answers from their peers, and contribute to the community by answering others’ questions.
• Brainly app covers a wide range of subjects, including math, science, history, and language arts. Students can ask questions about any topic related to these subjects.
• The app incorporates gamification features to encourage users to engage more in the community. Users can earn points for answering questions, and these points can be used to unlock new features.
• Brainly also offers premium features for users who want to access additional benefits, such as faster response times and personalized tutoring.

Table of Contents

What Is a Brainly App?

For parents, students, and teachers, Brainly is the world’s most popular learning and homework-help platform. Brainly allows users to get and offer support with complex homework difficulties and queries, allowing them to move from questioning to understanding.

For both students and professionals, an app like Brainly serves as a knowledge-sharing platform. Brainly delivers fast answers to any subject, including math, history, English, biology, advanced placement, health, business, computers, technology, and several languages.

Students can ask questions and receive specific replies via text, image, or voice. It leads individuals from inquiring to understanding and beyond by making education conveniently accessible, searchable, and even engaging.

According to CrunchBase, Brainly has raised $148.5 million in funding so far.

How Does a Homework App Like Brainly Work?

The app can provide homework support and assistance by employing AI technologies. There are numerous resources available, including videos, links, definitions, and Q&A.

Students need to click a photo of their homework, and the algorithm will handle the rest. This app is the best way to explore content on various subjects, particularly science and math.

Top Key Features of an App Like Brainly

If you want to develop a similar homework help app like Brainly, then you have to work more on its functionalities, or you can directly contact a top mobile app development company to identify top must-have features for your app.

Keep in mind that features can make or break your app. You need to be very careful while choosing the feature set of your custom homework app.

To help you in this, below are some key features that you can include in your homework help app like Brainly; take a look!

Parent Account

Parents can join their child’s Brainly learning experience by creating a Brainly Parent Account. The Parent Account can be linked to your child’s account to help them with homework, monitor their progress, and view their strengths and weaknesses. Any enhancements you purchase for your account will be transferred to your child’s account for free.

Multilingual Support

You must determine the primary and secondary languages spoken by your target audience when designing an educational app. You must then translate the app into those languages to ensure that none of your intended audience is left out.

Payment Gateway

F lexible payment models can make app monetization easier. To make revenue generation simple, the best educational applications accept all major credit cards, PayPal, Apple Pay, Google Pay, and other payment methods.

Gamification

If you’re making an interactive app for kids, you’ll almost certainly need to include a gaming feature. Individuals, on the other hand, can benefit from gamification as well. Students of all ages can stay focused and engaged by including gaming elements.

Push Notifications

Push notifications to assist you in keeping your scroll position and improve the number of times users visit the app. They can also generate a sense of urgency and encourage conversions. Make your push notification more lively by adding a text or video message. You can also include the user’s name and preferences to make them more tailored.

Create a Worksheet/Card

Teachers will build a worksheet based on the curriculum and share it with parents and learners. In addition, parents can review their report cards and discuss them with their child’s instructor. It will assist parents in recognizing their student’s strengths.

Content Customization

The content maker should be able to use various plugins to tailor the learning content. Learning efficiency can be improved by features such as text translation, emphasizing key features, and quick download of learning materials.

Smart Filters

The filters give users a good idea of how diverse the community is. Filters for school level, as well as primary and secondary grades, are available. Users can further refine their search by subject, and the app offers a wide range of options.

Offline Mode

Many students do not have access to the internet at all times. Offline way, where students can download a set of modules in one sitting, can keep students interested in the app and give the program benefits even when they are not connected to the internet. Every module they download will provide you with exact statistics on how successful the app is and how students respond to each lesson; this data is essential for course updates.

Conduct Tests

The aptitude to examine students is one of the most important characteristics of teachers. Online tests can be organized through the app. You can make a test with many questions and answers, multiple-choice questions, yes/no questions, and so on.

As you have seen, the features that you can include in your custom homework help apps are like Brainly. Now, let’s discuss the main component of this blog.

How to Develop a Homework App Like Brainly?

You need to take a few steps that you need to take while developing a homework app like Brainly. Here are some suggestions from our mobile app developers for making the development process simple.

Do Some Research

Before developing educational apps, you must first figure out what you want to do. The foundation of discovery is determining who your target audience is and what their needs are!

It would help if you chose who your app will be served to and which platform will be the most appropriate. Is your target audience more at ease with tablets, smartphones, or a desktop web app? You could also consider cross-platform app development, which allows you to deploy the same app across different platforms.

Also Read: Cross-Platform App Development|Everything You Need to Know

Design UI/UX

We all agree that the educational mobile app should be simple and easy to use, but how do you make that happen? As a result, you ensure that the educational mobile app’s UI is functioning correctly.

It should be basic, uncomplicated, and easy to comprehend. In order for users to return to the app, the design must be enticing and lively. Students and lecturers should find it intriguing enough to pay attention to the intricacies. In today’s world, users pay attention to even the finest detail in a mobile app.

Build a Team

Developing an app like Brainly is not an easy task. You need the right talents and expertise in your educational app development team.

To build an app like Brainly, you need the following members in your development team: –  

• Android app developers
• iOS app developers
• Front-end/backend developers
• UI/UX designers
• Project manager
• Business analyst
• DevOps Engineer

Choose the Right Tech Stack

You need to be mindful during choosing the technologies to build an app like Brainly, as it can directly impact the app development cost.

We’ve listed technologies we use to develop a custom homework app like Brainly. Take a look!  

• Languages – Kotlin, Java, Swift, Objective C, Python  
• Frameworks – NodeJS, Laravel, ReactJS, AngularJS, VueJS  
• Database – MySQL, MongoDB  
• Payment Gateway – PayPal, Stripe

MVP stands for Minimum Viable Product, which is basically a software product with minimum basic features, which enables businesses to test the viability of their idea. During the research phase, you can chalk out features and sort the basic features to include in development. MVP approach is best when you want to test your idea and attract investors. For the education sector, it’s better to consult an MVP development company for the initial stages, they can guide and help launch a homework platform like brainly.

Test the App

Testing is an unavoidable part of the app development process. Software testing’s major purpose is to verify that the application is not only easy to use and understand but also error-free.

Furthermore, bug-free software is essential for client satisfaction. Overall, quality assurance is an inescapable part of the eLearning app development process that assures that each product delivers outstanding value.

How to Monetize an App Like Brainly?

When it comes to monetizing your custom homework app like Brainly, you have a few approaches. However, the two most effective methods are: –

How Much Does Developing an App Like Brainly Cost in 2023?

Well, no mobile app development company can determine the exact app development cost before knowing the requirements.

The cost of developing a homework app like Brainly depends on various factors: –  

• Features & functionalities  
• Tech stack used  
• The hired eLearning development company (If you chose to outsource development)  
• App’s platform   
• App’s complexity  

If you want an in-depth quotation for developing an app like Brainly, then contact us. Our expert app developers would love to assist you.

Homework apps like Brainly are the new big thing in the education industry as the educational industry becomes more reliant on technology. Nowadays, individuals have begun to choose apps over all others.

Today is the time to invest in developing a homework-help app like Brainly. You must develop an app like Brainly that includes all the features mentioned above, plus anything more for which you can take a competitive edge.

If you’re interested in developing the ideal homework app, hire dedicated developers from us to get started. Share your requirements, and we’ll set up a free tech consultation to help you convert your idea into a reality.

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Facing Development Hurdles? Let’s Conquer Them Together. Our expert team is ready to tackle your challenges, from streamlining processes to scaling your tech.

INNOVATIVE BIG DATA PROJECTS

The term ‘Big Data’ refers to the process of managing the huge arrival of big data volumes, requests, or tasks from the user device to the storage platforms like Hadoop. In other words, the type of information, rather than its amount, determines if it is classified as Big Data or otherwise.  Therefore, big data projects are gaining a lot of importance owing to their tremendous speed in data processing, huge quantity, and diversity which also enables cost-efficient and creative data processing technologies to derive valuable perceptions.

This article provides a deep insight into the fundamentals and advances of Big Data which is very much essential for your research.

What is Big Data?

Big data is given the name because of its huge quantity of unstructured, semi-structured, and also structured data which can be mined while cannot be processed. The types of Big Data and their properties are given here for your reference, 

• Unstructured data include audio and video files
• Structured data includes SQL data store, schema-based data, and postgresql databases
• Semi-structured data consists of Json arrays and objects, tweets, weblogs, txt, csv and xlsx files

The Big Data engineers, analysts, developers, and writers with us have gained world-class certification and thus we are capable of offering the best research support to all Big Data projects. The following are the important characteristics of Big Data that are offered at the eight V’s of it, 

• Volume (quantity of the information that you are seeking to obtain)
• Veracity (confidentiality and accuracy of the data being dealt with)
• Variety (the nature of data)
• Viscosity (the action being called for or been stuck)
• Value (time-bound data availability)
• Visualisation (understanding ability and supporting decision making)
• Velocity (data gained and real-time opportunities to look for)
• Virality (data being conveyed or presented)

You can get tremendous information for your Big Data research from us. The conventional methods of data processing and analysis are being replaced by Big Data techniques at a large scale to improve the business and working of an organization. At this juncture, it is highly appreciated that you take up Big Data projects for your research . You can enjoy a quality research experience with the help of our experts and

Taxonomy of Big Data management

It is significant for a researcher to look into different aspects of Big Data management including the problems and potential solutions in it or its taxonomy as detailed below

• Issues –  high dimensional data storage, future data prediction difficulty, and delay in executing the process
• Solutions –  K means algorithm, Artificial bee colony, SQHAC, and optimization algorithms
• Issues –  inconsistency and lack of coordination and limitation in accessing speed
• Solutions –  Fuzzy Logic, dynamic data replication, optimization algorithm, artificial bee colony
• Issues –  Updating index in variable data, Delay in data retrieval and reduced result accuracy
• Solutions –  Fuzzy Logic, support vector machine, composite tree
• Issues –  traffic management constraints transmission speed limitations and capacity-constrained link
• Solutions –  orthogonal Frequency division multiplexing, traffic separating, and wavelength division multiplexing
• Issues –  Data duplication, missing and errors on inputs
• Solutions –  minimum covariance determinant, conditional functional dependencies and BIO – AJAX
• Issues –  constraints in processing large data sets, partitioning, and heterogeneity
• Solutions –  Statistics and Decision Trees
• Issues –  Constraints in data quality accuracy in prediction and real-time data processing
• Solutions –  Neural Network and Support Vector Machine

To get detailed information regarding the availability, scalability, integrity, heterogeneity, and velocity and resource optimization factors of these potential Big Data solutions respective to the issues stated above you can reach out to us at any time. We offer technically high-quality research data for your Big Data projects. The references from top journals will surely fetch you a great perception of all aspects of Big Data research. Let us now have a look into the limitations in Big Data

Research Challenges in Big Data

The following are the three primary problems that Big Data presents:

• As Big Data is really large, storing such huge quantities of information is a huge challenge.
• Obviously, it must guarantee that every item of information is securely stored and that there is no data leakage or any compromises occur in some other manner.
• When working with a large amount of data, it might be tough to draw information and insight, evaluate trends, and recognize similarities.

Our technical experts have guided a lot of Big Data projects and so we have got huge experience in handling these issues and challenges. Also, you can get any kind of query solved once you interact with our experts.  You can check out our website on Big Data for the potential solutions and remarkable breakthroughs that we recorded. What are the important Big Data techniques?

Big Data Techniques

The following are the common techniques used in Big Data analytics and all Big Data projects

• Factor, survival, classification tree, and principal component analysis
• Discrete choice, structural equation, hierarchical Bayes and time series models
• Optimization, support vector machines, dimensionality reduction, and neural networks
• Bayesian techniques and linear and nonlinear programming
• Multicriteria decision-making methods 
• Genetic algorithms and linear and non-linear regression

Usually, our experts provide you with all kinds of information required for handling these techniques. If you are looking for optimal procedure rules, protocols, steps, algorithms, and coding concerning any of these Big Data techniques then get in touch with our developers. We ensure to provide all necessary support in implementing codes and writing effective algorithms. In this regard let us have a look into the Big Data analytics algorithm below

Big Data analytics algorithms

• Q learning, compressed sensing, and dimensionality reduction
• Structured SVM, Relevance Vector Machines and temporal difference learning
• Random Forest, Linde-Buzo-Gray and Winnow algorithms
• Pulse coupled neural, radial basis function and Neural networks
• Locality sensitive hashing, backpropagation and reinforcement learning
• Numerical linear algebra, state action reward state action, and self organizing map
• Sketching, streaming, external data, and catch obliviousness
• ID3 extended 5 algorithm and Iterative Dichotomiser 3 or ID3 algorithm
• Decision trees, association rule learning, and association rule mining
• Eclat, meta Apriori and FP growth algorithms
• Bootstrap aggregating, AdaBoost adaptive boosting, and Brown Boost boosting algorithm
• Linear programming boosting (or LP boost) and logistic regression boosting (or LogitBoost)
• ALOPEX which is the Machine learning algorithm based on correlation and K nearest neighbor algorithm
• Zero attribute rule and one attribute rule algorithms
• Hopfield Net for Recurrent neural network and Perceptron for feedforward neural networks

Our experts are highly familiar with all these algorithms since we have got more than two decades of experience in Big Data projects. With the huge source of reliable research data that we provide to you regarding these algorithms and along with the real-time implemented examples you can have a complete picture of the use of all these algorithms. What are the Big Data platforms?

Big Data Tools List

A researcher in Big Data needs to have a complete idea of all Big Data tools and platforms. We provide numerous big data project ideas for research scholars.So let us see the prominent platforms for different processes involved in Big Data analytics below

• Presto and HAWQ
• IBM Big SQL and Impala
• Hive and Spark SQL
• S4 and Flink
• Storm and infosphere streams
• Tez and Flink
• Hadoop and spark
• Pregel and GraphX

Apart from these platforms, you need to have sound knowledge of the Big Data processing tools that you can use these platforms. 

• Compuware, AppDynamics, and VisualVM are the diagnostic tools
• Ganglia, JMX utilities, Zabbix and Nagios are the monitoring tools
• JMeter, Yahoo Cloud Serving Benchmark, and SandStorm are the performance test tools

For queries, regarding all the tools and platforms that we stated here, you can check out our website or connect with the technical team. We assure you that we provide you with full support in working with all these aspects . You can feel free to contact us at any time as we have got a customer support facility that functions day and night. Let us now look into the important applications of Big Data

Big Data Application Areas

• Decoding human brain based on deep learning 
• Collecting Big Data for a minimum period 
• Integration of data from everywhere and data federation
• Data access from all places in IoT analytics
• Abstraction of data and data virtualization

In addition to these applications, we find huge potential to be applied in many other fields including specific areas of space science, medical and military, and so on. We are here to offer the most reliable, trusted, and authentic research support in any topics related to Big Data so that your project can contribute one of the best applications to the field. In this respect let us now talk about Big Data research areas

Research Areas for Big Data Projects

• Distributed Fuzzy Decision Trees based data access
• Completely homomorphic encryption of cloud data
• Heterogeneous MapReduce for adaptive task tuning
• Real-time detection of spam drift and sentiment analysis in Twitter
• Describing extract actions and predicting social emotions
• Online social voting and machine learning-based disease diagnosis
• Smart City based on virtual reality
• Systems for Big Healthcare applications
• Management of information in smart grids
• Internet of Things and cloud integration in smart manufacturing systems
• Management of business processes
• Cloud-based digital entrepreneurship creation and processing data traffic
• Hazard rate based assessment of reliability and BaaS services resource management
• Evaluating data services quality and virtualized infrastructure protection

If you are searching for the best online research support on these project topics of Big Data research then you are at the right place. We are offering all kinds of research support like selecting a topic, designing the project by implementing codes and programming based on customization, checking for errors, upgrading the system, simulation, real-time implementation, recording the results, and so on. So you can reach out to us for any kind of technical assistance regarding Big Data projects. We now look into the recent trends in Big Data research

Current Trends in Big Data

• Scalability, effectiveness, structure, and mobility 
• Trustworthiness modeling and methodologies 
• Mining, networking, social media operations, and connectivity 
• Acquiring data, integrating, potential solutions and cleaning 
• Data pre-processing and mining based on semantics and social web searching
• Big velocity data applications in cloud, grid, and stream data meaning
• Graph mining, linking and multi-structured multimedia and big variety data

As we stated before we are one of the most trusted online research guidance providers in the world as we offer complete technical support to your Big Data projects . In this regard, it is quite important to mention that we are also providing other research-related supports like thesis writing, PhD research proposal in big data analytics , paper publication, assignment writing, proposal writing with a complete grammatical check, and an internal review. So you can get holistic research support from us. Let us now see other important research topics in Big Data

Top 7 Research Topics for Big Data Projects

• Big Data analytics in quantum computing
• Issues of privacy and security and graph databases
• Efficient modeling of uncertainty, data transfer, and storage
• Image, video, text, and other data processing based on real-time implementation of Big Data analytics
• Parallel data processing in scalable architecture and scalability
• Adopting and analysis using Big Data in cloud computing systems
• Cost-effective complex cloud computation analysis

These research topics in Big Data have been given to you by analyzing the long-term issues and concerns that are being addressed by Big Data solutions and the recent trends in its research. You can get the benchmark references that can be used to carry out big data projects and in-depth Research once you contact us. 

Why Work With Us ?

Senior research member, research experience, journal member, book publisher, research ethics, business ethics, valid references, explanations, paper publication, 9 big reasons to select us.

Our Editor-in-Chief has Website Ownership who control and deliver all aspects of PhD Direction to scholars and students and also keep the look to fully manage all our clients.

Our world-class certified experts have 18+years of experience in Research & Development programs (Industrial Research) who absolutely immersed as many scholars as possible in developing strong PhD research projects.

We associated with 200+reputed SCI and SCOPUS indexed journals (SJR ranking) for getting research work to be published in standard journals (Your first-choice journal).

PhDdirection.com is world’s largest book publishing platform that predominantly work subject-wise categories for scholars/students to assist their books writing and takes out into the University Library.

Our researchers provide required research ethics such as Confidentiality & Privacy, Novelty (valuable research), Plagiarism-Free, and Timely Delivery. Our customers have freedom to examine their current specific research activities.

Our organization take into consideration of customer satisfaction, online, offline support and professional works deliver since these are the actual inspiring business factors.

Solid works delivering by young qualified global research team. "References" is the key to evaluating works easier because we carefully assess scholars findings.

Detailed Videos, Readme files, Screenshots are provided for all research projects. We provide Teamviewer support and other online channels for project explanation.

Worthy journal publication is our main thing like IEEE, ACM, Springer, IET, Elsevier, etc. We substantially reduces scholars burden in publication side. We carry scholars from initial submission to final acceptance.

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How Brainly Powers Viral Marketing with Customer Feedback

You won’t know what your customers think unless you ask.

To make smart business decisions, you need customer feedback . But sometimes, staying in touch with your user base is easier said than done.

For companies with huge international audiences, the process of gathering, sharing, and analyzing feedback needs to be a well-oiled machine ready to operate on a massive scale. When you have millions of customers and opinions to account for, you need larger sample sizes.

At the same time, enterprise-level survey solutions are often a hassle to implement. And that doesn’t help small marketing teams that want to capture opinions about current events.

Those factors led Brainly , the global online learning platform, to turn to Survicate. Our tool lets them set up a multi-team surveying process. The customer data helps every department make more data-driven decisions and learn their audience’s current thoughts and insights.

We talked to Noah Berg, the Outreach Manager at Brainly, about how Survicate surveys help power his company’s marketing strategy. Noah told us:

• Why Brainly chose Survicate
• How Survicate helps Brainly create viral content (that doubled their blog traffic)
• How recurring surveys help them stay on top of user needs

If you’re looking for inspiration on how surveys can power your marketing – read on!

Why Brainly turned to customer feedback surveys 

Brainly is the largest online knowledge-sharing community of over 300 million students, parents, and experts. The company has users in over 35 countries, and with the funding of $150M, it’s an indisputable market leader.

Brainly’s staff knew that with a user base that large, they needed to capture feedback efficiently to keep up with their audiences’ needs. The sheer number of visitors going through their page every day made numerical user behavior data insufficient. Brainly needed to know the “why” behind the visitors’ actions to improve their platform, tailor the message to the users, and define their personas .

In the beginning, Brainly looked for a survey software that would let them ask focus groups about new product features . All product improvements at the company are heavily based on the users’ opinions, so collecting their feedback at scale was necessary to grow.

Survicate struck a perfect balance between ease of use and advanced features. The product team at Brainly appreciated the targeted website surveys that made it easier to segment the vast user base.

Soon, other departments realized that they could also use the power of customer feedback. And thanks to the unlimited seats and workspaces that let different teams share the same Survicate account, they quickly got on board.

Survicate: Unlimited users, speed, and reliability 

Brainly has been with Survicate since 2015. They have run over 800 surveys and gathered almost two million responses – with an average of a total of 25 790 responses every month. 

Currently, ten people at Brainly regularly set up their surveys (with more people being able to access the data). Here are their most common use cases:

• The product team uses Survicate to learn more about the motivations behind user behavior, collect feedback on new changes and gather product ideas
• The marketing team runs Net Promoter Score surveys to measure the loyalty of different customer segments and identify product issues
• The communications team uses surveys to gather data for content used in their PR and marketing campaigns. They also run a recurring “Brainly value” survey to stay on top of their users' opinions and impressions.

But there are more reasons why Brainly has stuck with Survicate for so long than just unlimited users and flexibility.

For Noah’s team, speed is of the essence. They want to capture their audience’s opinion on current events to prepare viral case studies and reports. At the same time, they want to reach as many respondents as possible. And Survicate lets Noah’s team launch website surveys fast and exactly where they want them – without the help of software developers or a dedicated research team.

Other than that, Survicate proved to be reliable and trouble-free. As Noah said: 

"Every time I've tried to contact support, it's been very quick. There are no issues nor missing features that I can think of.”

So now that we know why Brainly uses Survicate let’s drill down into the “how.” In this case study, we’ll focus on the two initiatives run by the outreach team: viral content creation and “the value of Brainly” survey. 

Free-to-use NPS survey template

Doubling the blog traffic with feedback-fueled reports

Noah noticed the potential of using customer feedback in his content marketing efforts right as he joined Brainly. With the number of students visiting their site every day, missing their feedback would have been a waste:

“When I joined, we were using [Survicate] to take opportunities, like with current events (...), to get some data from students, who are our core user base. We could use it to write interesting research-based articles.”

Survey data has been a starting point for many in-depth reports and infographics on Brainly’s blog. That already guaranteed Brainly the status of industry thought leader and generated steady media buzz around their content.

But Noah quickly understood that their reports needed to become more high-level to go viral. And the more frequently they appear, the more value they bring. Therefore, the surveys needed to be easier to answer so that they could gather a maximum number of responses in a short amount of time.

Noah’s team has gone for concise, close-ended surveys. The feedback they provide lets them quickly report on the most pressing issues.

One of the best examples of such content is Brainly’s report on students’ anxiety about returning to school during the pandemic . In 2021, as schools started reopening in the USA, the topic was on everybody’s lips. The media was full of politicians’, experts’, and teachers’ opinions. Yet one crucial voice was largely missing: the students’. Noah told us:

“Parents and students were really nervous about [going back to school]. And we got very unique insights – the student angle on that (...). The student voice about current events is often tough to get, especially on a mass level. And we were able to get that feedback very quickly, which was really cool. I think that became a theme for that year. Fear was at a high, and education was a huge question mark(...). Journalists and our readers found that really interesting.”

Brainly presented their findings in a report. They decided to share all the survey questions and answers, which resulted in a quick-to-write yet knowledge-packed report.

The report resulted in a boom in blog traffic that surprised the team. According to Noah, they got 1.8x the blog traffic they had expected. 

“We got plenty of reactions and conversations (...). That was certainly a big success that people remembered us talking about something important that matters to us.”

But how did Brainly manage to collect so many responses so quickly? It was all thanks to website surveys placed on the right pages.

Instead of limiting the respondents to users who were already signed up to Brainly, the outreach team launched website surveys on their most visited pages. The surveys fit naturally into the visitors' journeys and ensured a high response rate .

Feedback-based content helps Brainly create reports on current events relevant to their user base. This viral content fuels their PR and marketing efforts and creates media buzz around the brand. Survicate’s ease of use helps Noah’s team win the race with time.

Free-to-use content preferences survey template

Finding value proposition and gathering product feedback with “the value of Brainly” survey

Surveys are not just about catching the hottest topics for Brainly’s marketing teams. They also use them to gather feedback from their user base consistently.

As Noah told us, the online education landscape changes all the time. With lots of stakeholders and external factors influencing the niche, you have to keep the dialogue with your audience going to stay on top of their needs. It’s important to adjust buyer personas and value propositions constantly.

Recurring surveys help Brainly make sure their communication strategy is correct and give their audience exactly what they want.

Their biggest recurring survey, which Noah calls “the value of Brainly,” is run every six months and targets about 5.000 sample users (parents and students who are paying subscribers). It helps the marketing team see the subjective value of their products across several life stages of the customer base.

“The value of Brainly” survey starts with multiple-choice questions, such as:

• What do you use Brainly for?
• When do you use it?
• What is Brainly best for?

For each question, there’s an “other” option available. The respondents can type their own answers. 

These questions are followed by a series of statements that the respondents have to agree or disagree with (on a 5-point scale ranging from “completely agree” to “completely disagree”).

Here are the examples:

• Thanks to Brainly, I’m better prepared for school.
• Brainly helps me get higher grades.
• Brainly helps me finish my homework faster.

The last question is the open-ended “What is missing from Brainly?”. It lets the respondents freely voice their opinions and ideas.

The survey runs on Brainly’s website, and Noah uses targeting options to make it appear only to the desired audience segment.

The outreach team keeps the questions more or less the same in every survey iteration. It lets them effectively compare results over time.

The survey lets the outreach team:

• Understand how different user segments (e.g., parents and students, users on a higher-tier plan, and non-paying users) compare to one another – to improve communication and get ideas for targeted marketing campaigns
• Spot the most popular themes among all users to adjust the messaging on their website

As Noah told us:

“Doing homework is the most popular answer for ‘What do you use Brainly for?’ question. And that tells us we have to have a lot of keywords around ‘Homework help.’ It's our headline on our site. There are other things that kids can use us for, but people are sticking to the main use case, which is a good sign that our take on advertising is right. Some other answers, like ‘preparing for tests,’ ‘checking answers to questions,’ ‘learning more about subjects I'm interested in’ (...) are also popular, but not as much as the one that we put the most effort into.”

The recurring surveys also helped Noah’s team collect data points that become value propositions, selling points, or social proof for ad campaigns.

One more benefit comes from “the value of Brainly” survey: ideas for product improvements and new features. According to Noah, the respondents often leave their suggestions in the open-ended question text field. Then, the outreach team passes the insights to the product team.

Overall, “the value of Brainly” survey helps the marketing team connect with their audience and ensure their users get what they need.

Building a data-driven company with Survicate

As Noah told us, Survicate is the marketing team’s main data source.

With Brainly’s huge scale, quantitative data is not enough. To get to know their audience, they needed to know the “why” behind their actions and let them speak in their own voice. 

Survicate’s workspace organization and unlimited seats allowed them to overcome the challenge of running large-scale international surveys across different departments. At the same time, the ease of use still let them set up surveys quickly to gather insights about current events.

Brainly first used Survicate to look for product improvements. Still, it turned out the tool can fulfill all their customer feedback needs – measuring NPS, investigating the value of their product, and interviewing the audience for viral reports.

Survicate helped Brainly:

• Stay in touch with their user base
• Refine their value proposition, as well as the website and ad copy
• Double their blog traffic and increase social media hits and mentions
• Collect product improvement ideas

Now, it's all about you. Don't miss out on data-driven success - sign up for Survicate today!

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