Top 4 Interesting Big Data Projects In GitHub For Beginners
By Rohit Sharma
Updated on Jul 18, 2025 | 12 min read | 10.45K+ views
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By Rohit Sharma
Updated on Jul 18, 2025 | 12 min read | 10.45K+ views
Share:
Fun Fact: Apache Spark—one of the most popular big data frameworks—has over 19,000 GitHub contributors and surpassed 1000 contributors back in 2015, making it one of the most collaboratively developed big data projects on GitHub |
The top 4 interesting Big Data Projects in GitHub for beginners include a YouTube comment sentiment analyzer, a log parser using Hadoop, a real-time Twitter stream processor with Spark, and a movie recommendation engine. These projects teach core big data skills like distributed computing, data ingestion, and real-time analytics.
Each project introduces scalable data workflows and core concepts like batch vs stream processing. Beginners can gain portfolio-worthy experience by replicating or extending these practical, open-source solutions.
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Get started with four standout Big Data Projects in GitHub that beginners can build immediately. For example, Apache Spark, used by 80% of Fortune 500 companies, has over 2,000 GitHub contributors. The HiBench benchmark suite covers Hadoop, Spark, and streaming workloads like WordCount and K-means. Other top projects include a Spark-based Reddit comments pipeline and a student-built flight data analyzer, both offering practical experience with HDFS, Parquet, and real-time analytics.
To build industry-relevant Big Data skills for high-impact data roles, the following upGrad courses offer hands-on training in analytics, engineering, and automation.
Ready to get hands-on? Here are 4 beginner-friendly Big Data Projects in GitHub that you can start building right now.
Source: GitHub
Smartphones generate massive amounts of user, sensor, and app data—ideal for big data processing. This project uses the Lambda Architecture, combining batch (Hadoop/Spark) and real-time (Kafka + Spark Streaming) workflows to build a dynamic price prediction system. It demonstrates how to architect hybrid pipelines that can handle both historical data and live streams, which is a core challenge in data engineering.
This project allows you to simulate how businesses like eCommerce platforms or device manufacturers forecast pricing based on real-time market changes, customer preferences, and historical sales data. By implementing this model, you gain hands-on experience with scalable data ingestion and processing systems built for latency-sensitive analytics.
Use Case: Flipkart’s Real-Time Pricing Optimization
Flipkart dynamically adjusts smartphone prices based on real-time data like demand spikes, competitor pricing, and buyer behavior. Their internal systems use Spark, Kafka, and Hadoop to collect and process this data in near real time. This project mirrors that kind of logic, allowing you to build simplified but realistic simulations of a pricing engine. It also shows how businesses optimize revenue and user experience simultaneously through real-time analytics.
Key Skills You Will Learn
Project Prerequisites: Tools You Need for This Project
Tool |
Requirement |
Examples |
Apache Kafka | To stream real-time smartphone feature data into processing layers. | Confluent Kafka, Apache Kafka |
Apache Spark + Hadoop | For batch processing and distributed machine learning training. | Spark MLlib, HDFS |
Streaming Framework | To handle low-latency data processing in real-time layer. | Spark Structured Streaming, Flink (optional) |
ML Library | To build the price prediction model based on historical data. | Scikit-learn, XGBoost |
Visualization Tools | To build real-time dashboards that reflect price trends and alerts. | Apache Superset, Grafana |
Data Store | For storing batch-processed features and predictions. | Apache Hive, Cassandra |
Source: Domino
Traditional data tools like Pandas and NumPy struggle with large datasets due to single-threaded execution and memory limitations. This project uses Dask, a parallel computing library in Python, to scale familiar workflows across multiple cores or distributed clusters. It enables processing of large datasets that don’t fit in memory, with a syntax nearly identical to Pandas: ideal for beginners transitioning into big data.
You’ll work on transforming and analyzing large CSV or JSON datasets, building data pipelines that perform aggregations, joins, and transformations at scale. This project simulates real-world workflows in finance, health, or marketing, where quick batch calculations on large volumes of structured data are routine.
Use Case: Capital One’s Parallel Risk Model Evaluation
Capital One employs Dask to evaluate risk models across massive financial datasets using parallelized dataframes and scheduling systems. It helps their teams reduce computation time from hours to minutes during regulatory stress tests. This project allows you to simulate similar workloads—such as running statistical summaries across millions of records—using Dask’s distributed framework. The goal is to build scalable, maintainable workflows for business-critical analytics.
Key Skills You Will Learn
Project Prerequisites: Tools You Need for This Project
Tool |
Requirement |
Examples |
Dask | For scaling data operations and executing distributed workflows. | Dask DataFrame, Dask Delayed, Dask Distributed |
Pandas/NumPy | To prepare and validate operations before scaling to Dask. | Pandas, NumPy |
JupyterLab | For interactive data exploration and parallel processing testing. | Jupyter Notebook or JupyterLab |
Data Source | Large datasets to simulate parallel processing. | NYC Taxi Data, Kaggle Datasets, OpenML |
Visualization Tool | For profiling performance and cluster activity. | Dask Dashboard, Matplotlib, Seaborn |
Source: Github
In industries where data changes by the second, like finance, e-commerce, or logistics, real-time processing is crucial. This project uses Apache Storm, a distributed stream processing framework, to build a real-time pipeline using Spouts (data sources) and Bolts (processing units). Paired with Apache Kafka or file-based streams, you’ll build a topology that continuously ingests and processes data with low latency. It’s a hands-on way to learn stream-driven architecture used in high-frequency systems.
You’ll simulate scenarios like live transaction monitoring, fraud detection, or log analytics by processing event streams in real time. This helps you grasp the challenges of maintaining accuracy, consistency, and speed in distributed environments. The project teaches event queuing, fault tolerance, and how to scale processing with minimal delay.
Use Case: Twitter’s Storm-Based Trend Analytics Engine
Twitter historically used Apache Storm to process over 500 million tweets per day in real time to detect trending topics. The platform needed to identify viral content instantly, making Storm’s parallel processing and reliability critical. This project mimics that model by ingesting a live tweet stream (or a file simulation) and identifying trending terms or anomalies. It reflects how companies use real-time streaming to drive insights and act on data as it arrives.
Key Skills You Will Learn
Project Prerequisites: Tools You Need for This Project
Tool |
Requirement |
Examples |
Apache Storm | For building the core real-time stream processing topology. | Nimbus, Supervisor, Worker Nodes |
Apache Kafka | To simulate or handle real-time data ingestion through queues. | Kafka Producer/Consumer APIs |
Java or Python | Required for writing Storm components (Spouts and Bolts). | Java with Trident API, Python with Streamparse |
Data Source | Source of streaming data for real-time processing. | Twitter API, Log Files, Sensor Simulation |
Monitoring Tools | For tracking Storm job performance and debugging. | Storm UI, Grafana with Prometheus, JConsole |
Also Read: Explore the Top 10 Big Data Tools for Businesses
Source: Github
Handling time-series data at scale requires a high-throughput, low-latency NoSQL system—exactly what Apache HBase is built for. In this project, you’ll deploy HBase over Hadoop Distributed File System (HDFS) and design column-family-based tables optimized for time-stamped data. You’ll learn how to ingest large volumes of data, shard by row keys, and run aggregations or queries using MapReduce or Apache Phoenix. This project is ideal for simulating metrics platforms, financial tickers, or IoT sensor feeds.
You’ll create a time-series data model and store daily logs, events, or sensor data for rapid writes and efficient lookups. This project teaches how to structure row keys for read/write efficiency and how to manage data versioning in HBase. You’ll also work with compaction, region splits, and performance tuning—all key aspects of designing scalable data stores for time-sequenced records.
Use Case: OpenTSDB's Scalable Monitoring System on HBase
OpenTSDB (Open Time Series Database), built on top of HBase, stores billions of data points per day for large-scale monitoring systems. Companies like Yahoo and Salesforce have used it to monitor systems performance across thousands of machines. This project mirrors how OpenTSDB uses row-key sharding, time-bucketed writes, and bulk loading to scale efficiently. You’ll gain insights into how real businesses manage long-term observability and metrics storage using HBase.
Key Skills You Will Learn
Project Prerequisites: Tools You Need for This Project
Tool |
Requirement |
Examples |
Apache HBase | For storing and querying high-volume time-series data. | HBase Shell, HBase REST API, Java API |
Hadoop (HDFS) | Provides the storage layer for HBase to persist data. | HDFS with NameNode, DataNodes |
MapReduce Framework | To perform bulk data loads and aggregations. | Hadoop MapReduce jobs, BulkLoad tool |
Data Generator/Logs | Simulated or real time-series data source for ingestion. | IoT logs, server logs, or weather datasets |
Monitoring + Query Tool | To query or visualize stored time-series data. | Apache Phoenix, Grafana (via OpenTSDB), Hive |
Also Read: Understanding Hadoop Ecosystem: Architecture, Components & Tools
Choosing the right Big Data Projects in GitHub can boost your open‑source skills—in fact, 72–78% of companies contribute to big data open-source projects. GitHub now hosts over 150 million users and 5 billion contributions, meaning you’ll learn from a massive, active community. Beginners should look for repos with 100+ stars and recent commits to ensure quality and relevance. Focus on projects with clear READMEs, issue-tracking, and contributor guidelines—this mirrors real-world development workflows.
Here are some things to keep in mind when you’re looking for your first big data project in Github
1. Align with Your Career Goals
Choose a project that reflects the technical stack or domain of your target role. This helps you build skills relevant to job descriptions and interviews. Focused projects show recruiters you understand your own learning path.
Example: If you're targeting a data engineering role, pick a project involving Kafka, Spark, or Hadoop pipelines for real-time processing.
2. Choose the Right Scope and Complexity
Select a project that challenges you without overwhelming you. Avoid projects with vague scopes or enterprise-scale complexity unless they're well-documented. Define clear input/output, datasets, and tools before starting.
Example: Instead of building a complete analytics dashboard for all departments, focus on a customer churn prediction system using one dataset and a simple ML model.
3. Prioritize Active and Maintained Repositories
Pick GitHub projects with recent commits, active issue tracking, and responsive maintainers. This ensures you're working with up-to-date code and documentation. Engaging with active projects also helps you learn collaborative development.
Example: Avoid repositories that haven’t been updated in 2+ years; instead, choose one with recent merges and open issues tagged “good first issue.”
4. Look for Clear Documentation and Setup Instructions
Well-documented repositories reduce setup frustration and help you focus on learning core concepts. A detailed README, setup guide, and sample dataset are essential. It also reflects real-world practices of professional development.
Example: Projects that include setup scripts, data schemas, and usage examples are ideal for self-paced learning and faster onboarding.
5. Start with Projects That Use Familiar Tools
To avoid steep learning curves, begin with projects built on tools you already know—like Pandas, NumPy, or Jupyter. This lets you focus on big data concepts rather than tool configuration. As you grow, you can transition to distributed tools like Spark or Dask.
Example: Start with a Pandas-based project that migrates to Dask for scalability, instead of jumping straight into multi-node Spark deployments.
Also read: Top 10 Challenges of Big Data & Simple Solutions To Solve Them
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To thrive in Big Data, the top skills you need include data analysis and visualization, machine learning, cloud computing, and data engineering. These skills empower you to extract valuable insights, automate processes, and drive informed decisions.
In India, a skilled Big Data engineer can earn an average annual salary of ₹16.7 lakhs! So, if you're looking to enhance your skills, courses from upGrad can help you gain industry-relevant knowledge.
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Rohit Sharma is the Head of Revenue & Programs (International), with over 8 years of experience in business analytics, EdTech, and program management. He holds an M.Tech from IIT Delhi and specializes...
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