Top 20 Big Data Project Ideas for Beginners in 2021

Top 20 Big Data Project Ideas for Beginners in 2021


Have you ever looked for sneakers on Amazon and then later 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 have every 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. We bring to you the top big data project ideas for 2021 that are specially curated for students, beginners, and anybody else looking to get started with mastering data skills.

Table of contents

  1. Data Warehouse Design for an E-commerce Site
  2. Web Server Log Processing
  3. Generating Movie/Song Recommendations
  4. Analysis of Airline Datasets
  5. Real-time Traffic Analysis
  6. Visualizing Wikipedia Trends
  7. Analysis of Twitter Sentiments Using Spark Streaming
  8. Analysis of Crime Datasets
  9. Real-time Analysis of Log-entries from Applications Using Streaming Architectures
  10. Health Status Prediction
  11. Analysis of Tourist Behavior
  12. Detection of Fake News on Social Media
  13. Prediction of Calamities in a Given Area
  14. Generating Image Captions
  15. Credit Card Fraud Detection
  16. GIS Analytics for Better Waste Management
  17. Customized Programs for Students
  18. Visualizing Website Clickstream Data
  19. Real-time Tracking of Vehicles
  20. Analysis of Network Traffic and Call Data Records

What is Big Data?

More data is created every hour today than in an entire year just 20 years ago, according to the Seagate Rethink Data Survey by IDC, which was released in January 2020. According to the World Economic Forum, the amount of data in the world was estimated to be 44 zettabytes at the dawn of 2020. At the beginning of 2020, the number of bytes in the digital universe was 40 times bigger than the number of stars in the observable universe. As of October 2020, there were over 4 Billion internet users in the world. It’s no wonder that the term is referred to as “Big” Data.

Let’s get right into it. What exactly is Big Data? According to SAS, the term “Big Data” refers to data that is so large, fast, or complex that it’s difficult or impossible to process using traditional methods. Big Data is said to be classified based on the 4 V’s which are:

  • Volume: The quantity of data that has to be processed.
  • Variety: The type and nature of the data. Data may be in the form of text, images, videos, log files, audio, sensors, etc., and may flow in batches, streams, or both. Data can be structured, unstructured, or a mix of both
  • Velocity: The speed at which data has to be collected and/or processed.
  • Veracity: This refers to the reliability and the quality of the data. In many cases, raw data has to be processed, which may require filtering out data that is not required.

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In the field of Big Data, the aim is to process these large amounts of data and make sense out of the data, find patterns or associate some value to the data. By doing so, businesses can optimize their models to better meet the needs of the users. This means that there is no point in solely collecting data. It has to be processed for the maximum benefit. According to McKinsey, retailers could stand to increase their operating margins by at least 60% with the application of Big Data (). According to Forbes, over 90% of companies state that they are still facing the need to manage unstructured data. Big Data has made its mark in many fields, and the sectors which have yet to use the applications of Big Data are doing so now.

If you are a newbie to the field of 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 useful way of learning a skill is with some hands-on experience. Given below is a list of Big Data project ideas and an idea of the approach that you could take to develop them, in the hope that this could help you learn more about the field of Big Data and even kick-start a career in Big Data. If you already have some experience in the field but are looking to take it up a notch, there are some more complex projects mentioned as well. In addition, I’m sure you might be surprised with the level of reach that Big Data has managed to achieve across multiple sectors, and who knows, maybe one of the ideas could inspire you to come up with some of your own!

Top 20 Big Data Project Ideas For 2021

Simple Big Data Projects Ideas for Beginners and Students with Source Code

    1. Data Warehouse Design for an E-commerce Site:

A data warehouse is essentially a large collection of data for a business that helps the business make informed decisions based on the analysis of the data. For an e-commerce site, the data warehouse would be a central repository, which consists of consolidated data ranging from searches to purchases made by site visitors. By designing such a data warehouse, the site can manage supply based on demand (inventory management), take care of their logistics, modify pricing for optimum profits and manage advertisements based on searches and items purchased. Recommendations can also be generated based on patterns in a given area or based on age groups, sex, and other similar interests. While designing the data warehouse, it is important to keep in mind some key aspects such as how the data from multiple sources can be stored, retrieved, structured, modified, and analyzed. If you are a student looking for Apache Big Data projects, this is a very good place to start since this project can be developed using Apache Hive.

     2. Web Server Log Processing:

A web server log maintains a list of page requests along with activities it has performed. Storing, processing, and mining the data on web servers can be done to further analyze the data. 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 is beneficial to any business that heavily relies on its website for revenue generation or to reach out to its customers. A Hadoop ecosystem that consists of tools such as Pig, Impala, Hive, Spark, Kafka Oozie, and HDFS can be used for storage and processing.

     3. Generating Movie/Song Recommendations

Streaming platforms can most easily appeal to their audience based on recommendations. By continuously generating recommendations suitable for a particular individual, engagement on the platform can be maximized. Content can be recommended based on multiple approaches – based on previous watches, based on demographics, newest and trending movies, based on searches, and based on ratings from other individuals who have watched a movie or listened to a particular song. The datasets must be gathered and then grouped based on these factors to find patterns. Projects requiring the generation of a recommendation system are an excellent idea if the goal is to find Big Data projects on a slightly intermediate level. The use of Spark SQL to store the data and Apache Hive to process the data along with a few applications of machine learning can build the required recommendation system.

     4. Analysis of Airline Datasets:

It is important for large amounts of data from any site to be processed and analyzed so that they can become useful to the business. This is another excellent choice if one is looking to find Big Data analytics projects for students. In the case of airlines, popular routes will have to be monitored so that more airlines can be available on those routes to maximized efficiency. Does the number of people flying across a particular path change over the course of a day/week/month/year and what factors can lead to these fluctuations? In addition, it is also necessary to closely observe delays – are older flights more prone to delays? When is the best time of the day/week/year/month to bring delays to a minimum? Focus on this data helps the airlines and the passengers using the airlines as well. In this case, Hive/Impala may be used for partitioning and clustering of the data. Apache pig can be used for data preprocessing.

A simple big data project idea for students on how to perform analysis of airline datasets is here 

     5. 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 be used to program traffic lights at junctions too – stay green for a longer time on higher movement roads and less time for roads that are showing less vehicular movement at a given time. Real-time traffic analysis can help businesses manage their logistics and for working-class individuals to plan their commute accordingly. Concepts of deep learning can be used to properly analyze this dataset.

Big Data Projects

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

    7. Analysis of Twitter Sentiments Using Spark Streaming

Sentimental analysis is 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 larger reach by getting an idea of the feel of the customers. From a political standpoint, the sentiments of the crowd to a candidate or to some decision taken by a party can help determine what keeps a certain group of people happy and satisfied. Twitter sentiments can be used 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. For sentimental analysis, NLP (Natural Language Processing) models will have to be used. The models will have to be trained with some prior datasets. Sentiment analysis is one of the more advanced projects that can be done using Big Data due to its involvement in NLP as well.

      8. Analysis of Crime Datasets

In the public sector, it is found that Big Data has its applications in detecting patterns in crimes. 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 a very good option for a Big Data project idea 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.

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

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 in finding more content that can keep the users engaged. Real-time analysis can also help to immediately detect a security breach and take necessary action. 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 be used to process real-time streaming data.

      10. Health Status Prediction

“Health is wealth” is a very popular saying. And rightly so, there cannot be wealth unless one is healthy enough to enjoy the worldly pleasures. Many diseases have risk factors associated with them which can be genetic, environmental, dietary and also more common for a certain age group or sex and more commonly is seen in some races or in particular areas. By gathering datasets of this information relevant for particular diseases e.g., breast cancer, Parkinson’s disease, 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 the 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.

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      11. Analysis of Tourist Behavior

Tourism is a large sector that provides the livelihood for several people and can adversely impact the economy of a country. By analyzing tourist behavior, it is possible to enhance tourism planning, participation and marketing can be modified to not only help the people whose livelihood is affected by tourism but also to keep the tourists content and allow them to have a well-spent vacation. Hotels and itineraries can also be recommended. Not all tourists behave in the same manner 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.

      12. Detection of Fake News on Social Media

With the popularity of social media, a major concern that has arisen is the spread of fake news on various sites. Even worse, this misinformation tends to spread even faster than factual news. According to Wikipedia, fake news can be visual-based, which refers to images, videos, and even graphical representation of data, or it can be 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. This data must be processed to determine the validity of the post. 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.

      13. Prediction of Calamities in a Given Area

Certain calamities such as landslides and wildfires are seen to occur more frequently during a particular season and more so in certain areas. By making use of certain geospatial technologies such as remote sensing and GIS (Geographic Information System) models, it is possible to monitor areas that are 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 local residents from these calamities, 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.

      14. Generating Image Captions

With the emergence of social media and the importance of digital marketing, it has become important for businesses to upload engaging content. Catchy images are definitely 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 captions that are relevant but also appealing. 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 to this.

Hadoop and Spark Projects

      15. 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 mostly private. Since this project involves machine learning, the results will be more accurate with a larger dataset. The data availability can pose a challenge in this manner. Credit card fraud detection is helpful for a business since customers will tend to trust a business with better fraud detection sites and naturally for the customer as well since he/she will not be billed for purchases made by someone else. Fraud detection can be considered to be a Big Data project for students and beginners.

      16. GIS Analytics for Better Waste Management

Due to urbanization and population growth, large amounts of waste are being generated on a global level. 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 modelling to ensure that waste is picked up, transferred to a transfer site and ultimately reaches the landfills or recycling plants in the most efficient manner. GIS modelling can also be used to select the best sites for landfills. Location and placement of garbage bins within city localities has to be analyzed as well. Sustainable waste management has become a very important issue that has to be tackled. Here too, the prerequisite of some knowledge on remote sensing and GIS is needed to take up this project.

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      17. Customized Programs for Students

As individuals, we all tend to have different strengths and different paces of learning. There are different kinds of intelligence and the curriculum only tends to focus on a few of these things. With Big Data, it can be modified to gather data from students across various streams and modify academic programs to better nurture students. Programs can be designed based on a student’s attention span and can be modified to go according to an individual’s pace, which can be different for different subjects. For e.g., one student may find it easier to grasp language subjects but struggle with mathematical concepts while another student might find it easier to work with math but not be able to breeze through language subjects. Customized programs can boost student’s morale, which could also reduce the number of dropouts. Teachers will also have an idea of which students require more attention and can accordingly work with the students at the required pace. Schools that offer this in their curriculum will be prioritized since it’s a win-win for the school and the students. Analysis of a student’s strong subjects along with monitoring their attention span and their responses to certain topics in a subject can help build the dataset to create these customized programs.

      18. Visualizing Website Clickstream Data

Clickstream data analysis refers to the process of collecting, processing, and understanding all the web pages a particular user visits. This kind of analysis has benefits for 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, helps the business which is publishing the ad to reach the customer, and at the same time reach internet users. This can be classified as a Big Data Apache project by using Hadoop to build it.

      19. Real-time Tracking of Vehicles

Transportation plays a major role in many activities. Every day, goods have to be shipped across cities, countries too, kids commute to school, 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, at present very little amount of this data is currently being used for actually improving 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 because these large amounts of data have to be processed in real-time. With a deeper analysis of the data, telecom industries can make decisions that can improve the customer experience by monitoring the network traffic. Issues such as call drops and network interruptions have to be closely monitored so that they can be addressed accordingly. By evaluating 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.

Master Big Data Skills With Big Data Projects

We hope you were able to find some new ideas for projects in this list. The source codes for many of these projects can be found on our website, along with tutorials and explanation of the tools used in the projects so that you can get an in-depth understanding of how and why the data has to be processed in the way that it is and you can even use a similar approach for different applications. Get started now and build your career in Big Data from scratch if you are a beginner, or grow it from where you are at present. Remember, it’s never too late to learn a new skill, and even more so a field which has so many uses at present and even then, still has so much more to offer. We hope that some of the ideas inspire you to come up with ideas of your own. The Big Data train is chugging at a very fast pace and it’s time for you to hop on, if you aren’t on it already!

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