Top 4 Interesting Big Data Projects In GitHub For Beginners

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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4 Big Data Projects in GitHub You Can Build Today!

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.

• Executive Diploma in Data Science & AI
• Generative AI Mastery Certificate for Data Analysis
• Master’s Degree in Artificial Intelligence and Data Science

Ready to get hands-on? Here are 4 beginner-friendly Big Data Projects in GitHub that you can start building right now.

1. Smartphone Price Predictor (Lambda Architecture)

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

• Lambda Architecture Design: Learn to separate batch and streaming layers while maintaining consistency and scalability.
• Data Ingestion Pipelines: Set up producers and consumers using Kafka for real-time data feeds.
• Distributed Processing: Implement data transformations and aggregations using Apache Spark and Hadoop.
• Predictive Modeling: Train machine learning models on historical data and adapt them to streaming inputs.
• Real-Time Dashboarding: Use tools like Apache Superset or Grafana to visualize predictions and alerts.

Project Prerequisites: Tools You Need for This Project

2. Dask Parallel Computing Project

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

• Parallelized DataFrame Operations: Scale Pandas workflows across CPUs or clusters using Dask’s familiar syntax.
• Task Scheduling and DAG Execution: Understand how Dask’s task graph execution optimizes performance.
• Memory-Efficient Processing: Work with out-of-core datasets using Dask’s chunked computation engine.
• Cluster Deployment Basics: Set up and run local and distributed clusters for Dask task execution.
• Performance Profiling: Use Dask’s diagnostic dashboards to analyze bottlenecks and optimize execution.

Project Prerequisites: Tools You Need for This Project

3. Apache Storm Real-Time Streaming App

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

• Storm Topology Design: Build end-to-end pipelines using Spouts (data inlets) and Bolts (data processors).
• Stream Processing Concepts: Understand event-time vs processing-time logic and how to manage latency.
• Kafka Integration: Use Kafka as a reliable message broker for event queues and ingestion.
• Stateless vs Stateful Processing: Learn to manage transformations, counters, and aggregations in distributed systems.
• Fault Tolerance and Scalability: Ensure uptime and recovery by configuring Storm’s fault handling and parallelism.

Project Prerequisites: Tools You Need for This Project

Also Read: Explore the Top 10 Big Data Tools for Businesses

4. Apache HBase Time-Series Data Store

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

• Column-Family Table Design: Learn to structure HBase tables for fast writes and optimized scans across time-series data.
• Efficient Row Key Sharding: Understand how to prevent hotspotting and achieve even data distribution.
• Bulk Write Optimization: Use MapReduce to batch insert large datasets efficiently.
• Data Versioning and TTL: Control how long data is retained and queried using time-based filtering.
• HBase-Hadoop Integration: Build pipelines that ingest data into HBase using HDFS and query using Phoenix or Hive.

Project Prerequisites: Tools You Need for This Project

Also Read: Understanding Hadoop Ecosystem: Architecture, Components & Tools

Tips For Choosing The Right Big Data Projects In GitHub For Beginners

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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Wrapping Up!

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. 

Here are some additional courses to help you stay relevant: 

• Professional Certificate Program in Data Science and AI with PwC Academy
• Data Structures & Algorithms
• Advanced SQL: Functions and Formulas

Feeling unsure about which Big Data skills to focus on? Get personalized counseling to guide your learning journey. Visit our offline centers for expert advice and tailored course recommendations to help you succeed.

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