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What is Big Data Project? [with Examples]

26th Apr, 2024
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    What is Big Data Project? [with Examples]

    Big Data is the huge, diversified data volume that keeps growing at an exponential rate, coming from different sources both in structured, unstructured and semi-structured forms. Businesses evaluate this data to gain insights to make better business decisions that otherwise would have been impossible in the present age of technology and business landscape. But it is not the quantity of data that is important. The proper use of it for gaining a perception of what businesses do with such data is important. Big Data influences an individual’s daily life as well as it is experienced every day by way of social media, shopping, entertainment, health, and hordes of other things.

    What is Big Data?

    Big Data is characterized by the “three V’s” denoting Volume, velocity or the speed with which it gets created/collected, variety or the various types of data. 

    Examples of Big Data: Data from social media (texts, numeric, images, video, comments), data from IoTs (internet of things) and others. 

    Big Data: The Features

    1. Huge in volume, generated at an exponential rate with huge variety, comes from different sources and can be structured, semi-structured or unstructured. 
    2. Structured data includes numeric or alphanumeric and is formatted. 
    3. Unstructured data includes non-numerical, free form, less quantifiable and difficult to format and store. 
    4.  Big Data influences every function/department in a company, governments throughout the world, defense, space, healthcare and even personal lives are impossible without them. 
    5. Can be collected through personal devices (for example video, voice recordings), applications, questionnaires and various other ways. 
    6. Organizations generally collect Big Data in computer databases, servers etc. and examine it with software to derive meaningful insights from it, which is the main use of Big Data. 
    7.  Any data set exceeding a terabyte would be considered Big Data.  
    8.  Data Engineers, Software Engineers, Statisticians, Data Hygienists, Data Architects, Data Scientists, Visualizers and Business Analysts are the people who work with Big Data projects using Hadoop. Often they take Big Data and Hadoop Certification to facilitate their work.

    What Is Big Data Project?

    A Big Data project is the work of data analysis that uses a variety of very large raw data sets as the foundation for its analysis. Such Big Data analytics projects combine both traditional data analysis techniques and also modern ones that are specifically designed to handle large data volumes. Big Data projects typically use deep learning, machine learning, convolutional neural networks (the deep learning algorithms that take images as input for image recognition and classification), and computer vision as a part of the data analysis process. 

    Some interesting Big Data project names include Big Data for cybersecurity, anomaly detection in cloud servers, malicious user detection in Big Data collection, tourist behavior analysis.

     The data engineers working on any Big Data domain project or Big Data related projects must first acquire proficiency in areas like machine learning, data visualization, data analytics, deep learning etc, and also pursue Big Data Certification Courses to enhance their skills.

    GitHub and ProjectPro are some of the platform examples that offer different types of Big Data projects list, including Big Data simple projects, easy Big Data projects, and also Big Data small projects meant for professionals at different skill levels - beginner, intermediate, and advanced.

    Some Big Data sample projects examples are: 

    1. Fruit Image Classification 

    Source Code: Fruit Image Classification

    1. Criminal Network Analysis

    Source Code- Criminal Network Analysis

    Big Data Project’s Goal?

    The goal of any Big Data analysis project is to do data mining (the process of searching large datasets for patterns, like weather forecasting that analyzes historical data to identify patterns and predict future weather conditions) and analyze the data to uncover underlying patterns and derive insights. Big Data project examples include e-commerce or banking sectors that use Big Data projects to understand customer behavior, trends and formulate business strategies aligned to the insight received from the project.

    Big Data Projects Problem Solving Process

    The steps of a Big Data project are as follows:

    Big Data projects problem solving process

    1. Define problems (by decoding the business/project goals)

    Understanding the business or the industry is the foundation of any good Big Data analytics project. This includes:

    • Meeting all related individuals whose processes are needed to be data transformed and analyzed.
    • Identifying a definite purpose or goal of what needs to be done with the collected data (like a specific problem that needs to be solved, a data product to be built etc.).
    • Establishing a timeline and specific key performance indicators.

    2. Source the Data (Data collection)

    Gathering raw data from various sources is the next step. Some options for data collection could be:

    • Utilizing already existing databases public or private.
    • The APIs for all the tools that the company has been utilizing and the data gathered by them need to be considered.
    • Open data platforms may also be considered if required.

    3. Clean the Data (Data preparation)

    This is the most time-consuming step of the project. This includes: 

    • Examining and analyzing the collected data. 
    • Talking to relevant people like the IT team or other groups might be needed to understand the relevancy of all the data and discard the irrelevant ones.
    • Checking for data errors, missing data values etc. is the next step.
    • Ensuring that the data privacy protocols of the organization are strictly followed is a major task of the data preparation stage.
    • Storing all the data sources and data sets into one location/platform to expedite governance and carry out privacy-compliant projects.

    4. Analyze the Data (Data modification/transformation)

    Now the cleaned data needs to be transformed or modified to extract useful information. This includes:

    • Combining all the various data sources and group logs.
    • Gathering all the data-related elements like a month, hour, day, week, year etc.
    • Calculating the variations.
    • Joining datasets (for example extracting columns from one set of data and adding them to another dataset). This could be challenging in the case of dealing with many data sources.

    5. Build Data Visualizations

    This involves data analysis by creating beautiful dashboards, charts, or graphs that data visualization tools offer. This is especially important when massive volumes of data analysis need to be showcased. For example, after plotting data on a map, it could come into focus that some small geographic regions provide more information than some large cities or even nations. This is only possible if proper data visualization is done.

    6. What Is the Outcome?

    This is the last step of a Big Data project and entails a) an analysis to reveal certain patterns in the data or b) helping solve a specific business challenge.

    The analysis results are then presented with the help of various visualization tools to make it easy to understand by everyone.

    Big Data Projects: Why are They so Important?

    Big Data projects are crucial to companies. Here are some statistics: 

    • Netflix saves $1 billion per year on customer retention using Big Data. 
    • The US economy loses up to $3.1 trillion yearly due to poor data quality. 

    Businesses learn everything about their customers like what they want, who their best customers are, customer behaviors, why people choose different products and plenty of other such information. 

    By 2025 the world is going to generate 181 zettabytes of data, as per predictions. This could be a gold mine for businesses that can successfully decipher Big Data and extract actionable insights. The more information or insight a company gets, the more competitive it becomes in the market. In the long run, it improves its balance sheet and profitability. 

    Big Data project insights can be combined with machine learning to create market strategies and become more customer centric. 

    Big Data Project Ideas

    We will explore some Big Data projects with source code that you could explore and do as well to include in your data science portfolio. We will cover Big Data projects for beginners, intermediate and advanced levels so that you can choose the one that is right for you. 

    1. Beginners Level

    • 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 
    • Yelp Data Processing Using Spark And Hive Part 1 
    • Yelp Data Processing using Spark and Hive Part 2 

    2. Intermediate Level

    • 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 

    3. Advanced Level

    • 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

    Big Data Projects Examples for Beginners

    We are discussing here a couple of Big Data analytics projects with source code for beginners. Beginners can learn Big Data Analytics Online. They can use them as Big Data practice projects. 

    1. Data Warehouse Design for an E-Commerce Site

    • The purpose: A data warehouse is a repository of huge data volumes of a company. The repository is used for the company to make informed business decisions post-data analysis. 
    • The company: The implementation of the data warehouse is for an e-commerce website “Infibeam” which is into the sales of digital and consumer electronics. 

    The requirement: 

    1. The project needs to design a central repository for the company containing all the data gathered from searches to purchases made by site visitors. 
    2. The site needs to manage inventory (supply based on demand), logistics, price (with maximum profitability) and advertisements based on searches made and things purchased by creating such a data warehouse. 
    3.  Recommendations based on areas, age, sex and other shared interests can also be made. 

    <Source Code – Data Warehouse Design> 

    2. Search Engine

    • The purpose: To get an insight into what people are searching for in search engines. 
    • The company:  search engines 

    The requirement: 

    1.  A full-featured search engine built on top of a 75-gigabyte. 
    2. Use of datasets like stopwords.txt (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). 
    3.  Addresses latency, indexing, and huge data concerns with code and the K-Way merge sort method. 

    <Source Code – Search Engine> 

    What Makes a Good Big Data Project?

    The best Big Data projects will have the following attributes: 

    1. Quality First

    The importance of an end- to- end Big Data project does not lie in quantity but in how much meaningful insight a business derives out of it that can support business objectives and inform business decisions.  

    This calls for scrutinizing a variety of data sources to obtain raw data from, choosing the right algorithms to process it and interpreting the results correctly. 

    2. Clarity: Meaningful Outcome Alone

    The main objective of any Big Data based projects is the quality of the insight derived from it and its impact on the business to meet the business objectives. Maximizing value and not volume (or using fanciful technologies) should alone be the clear focus of any Big Data project. This is the reason why a good Big Data engineer also needs to be business savvy with the ability to combine technical jargon with business understanding and strategy. 

    3. Perfect Coding and Analysis

    Any good Big Data project or any Big Data major projects will have clean and accurate coding that is with the right formatting and comments kept wherever required. This makes it easy to understand for all involved in the project. The analysis must be free from biases and emotions to make it perfectly accurate. 

    How to Leverage Your Big Data Projects?

    1. Ensure to upload your code on platforms like Github, Bitbucket, GitLab, SVN(Subversion) etc. Recruiters will like to examine the code a prospective candidate produces.
    2. Build you portfolio and reposit all your work there. Portfolios now have become an integral part of the candidate selection process therefore having one is a must.
    3. Mention some of your projects briefly in your resume and ensure to mention only the projects that are relevant to your targeted job.

    What Problems You Might Face in Doing Big Data Projects?

    A data analyst might come across quite a few challenges while executing Big Data projects, especially the Big Data live projects or some real time projects on Big Data. These are:

    1. Inadequate Monitoring

     While working with Big Data real-time projects, monitoring real-time environments could be a problem as not many solutions are available for this.

    2. Latency Problems

    Output latency during data virtualization is a common problem faced during data analysis due to the tools requiring high-level performance leading to latency in output generation.

    3. Data Privacy

    While dealing with data, data privacy and the governance policy of the company needs to be adhered to as any privacy breach to it might be fatal to the project.

    4. Demanding Scripts/ Tools

    A Big Data analytics project might require a higher-level of scripting or the use of tools that you are not familiar with.


    Jobs in the data science field will increase nearly by 28% to 30% as per the technology predictions of 2022, creating nearly eleven million new jobs. Business leaders who will explore and efficiently utilize the colossal benefits of Big Data insights will remain ahead of their competitors. New roles will come up to close the gap between high demand and low supply of professionals, especially in sectors where the demand is high. Those who will be growing their skills in Big Data will experience a high trajectory in their career growth.

    Frequently Asked Questions (FAQs)

    1How Long Will a Big Data Project Take to Complete?

    It depends on various factors like data volume to be handled, project team size and processing time. Typically it might take between two weeks to six months. 

    2Are Big Data Projects Necessary to Land a Job?

    Most definitely yes! Also, ensure to upload your code to platforms like Github or others for recruiters to see. 

    3How many Big Data projects fail?

    Gartner 2017, mentioned 85% of Big Data projects fail   

    • As per VentureBeat 2019, 87% of data science projects never make it to production. 
    • Gartner 2019, mentioned only 20% of analytic insights would deliver any business outcomes. 
    4What industries use Big Data?

    Every industry uses Big Data now, including government, space, defense, healthcare, banking and financial enterprises and e-commerce just to name a few. 

    5What are the 7 V's of Big Data?

    Volume, velocity, variety, variability, veracity, and visualization.


    Dr. Manish Kumar Jain

    International Corporate Trainer

    Dr. Manish Kumar Jain is an accomplished author, international corporate trainer, and technical consultant with 20+ years of industry experience. He specializes in cutting-edge technologies such as ChatGPT, OpenAI, generative AI, prompt engineering, Industry 4.0, web 3.0, blockchain, RPA, IoT, ML, data science, big data, AI, cloud computing, Hadoop, and deep learning. With expertise in fintech, IIoT, and blockchain, he possesses in-depth knowledge of diverse sectors including finance, aerospace, retail, logistics, energy, banking, telecom, healthcare, manufacturing, education, and oil and gas. Holding a PhD in deep learning and image processing, Dr. Jain's extensive certifications and professional achievements demonstrate his commitment to delivering exceptional training and consultancy services globally while staying at the forefront of technology.

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