Top Hadoop Projects for Beginners in 2024

Top Big Data Hadoop Projects for Practice with Source Code- Here are some hadoop projects for beginners to practice that will help build a project portfolio.

Top Hadoop Projects for Beginners in 2024
 |  BY ProjectPro

In the big data industry, Hadoop has emerged as a popular framework for processing and analyzing large datasets, with its ability to handle massive amounts of structured and unstructured data. As businesses continue to show interest in leveraging their vast amounts of data, Hadoop projects are becoming increasingly important for organizations looking to extract insights and gain a competitive edge. In this blog, we will explore some exciting and real time Hadoop projects that can help you take your data analysis and processing to the next level. From social media analytics to healthcare data analysis, we will dive into the details of these projects and learn how they are leveraging Hadoop to tackle complex data challenges.


Hadoop Project-Analysis of Yelp Dataset using Hadoop Hive

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Why work on Apache Hadoop Projects?

Apache houses a number of Hadoop projects developed to deliver scalable, secure, and reliable solutions. Hadoop Common houses the common utilities that support other modules, Hadoop Distributed File System (HDFS) provides high throughput access to application data, Hadoop YARN is a job scheduling framework that is responsible for cluster resource management and Hadoop MapReduce facilitates parallel processing of large data sets.

Big Data Hadoop Projects

A number of big data Hadoop projects have been built on this platform and this has fundamentally changed a number of assumptions we had about data. Hadoop looks at architecture in an entirely different way. As Big Data Hadoop projects make optimum use of ever-increasing parallel processing capabilities of processors and expanding storage spaces to deliver cost-effective, reliable solutions; they have become one of the must have big data skills that one must possess if they want to work on any kind of big data project.

30+ Real-Time Hadoop Projects

Apache Hadoop is one of the most prominent technologies in the world of Big Data Analytics. Hadoop came as a rescue when the data volume coming from different sources increased exponentially. This Big Data technology is one of the most cost-effective storage solutions currently available. On top of that Apache Hadoop is also known for its reliability and scalable features.

Let us now start with our exciting list of projects in Hadoop and Big Data. The projects have been divided into various categories for your convenience.

Hadoop Projects for Final-year Students

This section contains Hadoop sample projects that are meant for students’ final year projects.

  1. Designing a Hadoop Architecture

Before starting any Hadoop project, it is important that you understand the Hadoop architecture. And, as they say, knowledge without action is incomplete; it is thus crucial that you build a project that teaches how to design a Hadoop architecture. 

Problem Statement

In this Hadoop project, you will understand the basics of Hadoop architecture by designing it. Consider the case of a web server that produces a log file with time label and query; you will learn how to fetch the top 15 queries generated in the last 12 hours.

What will you learn from this Hadoop Project?

  • How to design Hadoop architecture?

  • Storing data in Hadoop by using data acquisition tools.

  • Writing real-time queries in Apache Hive.

  • Fetching data through Apache Hadoop.

Access the solution to the Hadoop Project -Design a Hadoop Architecture 

ProjectPro Free Projects on Big Data and Data Science

2. Visualising Website Clickstream Data with Apache Hadoop

Problem: Ecommerce and other commercial websites track where visitors click and the path they take through the website. This data can be analysed using big data analytics to maximise revenue and profits. 

Big Data technologies used: AWS EC2, AWS S3, Flume, Spark, Spark Sql, Tableau, Airflow

Big Data Architecture: This implementation is deployed on AWS EC2 and uses flume for ingestion, S3 as a data store, Spark SQL tables for processing, Tableau for visualization, and Airflow for orchestration. 

Hive Project - Visualising Website Clickstream Data with Apache Hadoop

3. Handling small files using Hadoop

If you have explored Hadoop architecture thoroughly, you must know that Hadoop’s Distributed File System (HDFS) was fundamentally designed for handling large files. It is not an easy task for HDFS to read through small files as it involves a large number of hops from one datanode to another and plenty of seeks. Hence, it is a challenge to form an efficient data access pipeline for small files (size is smaller than the HDFS block size) using Hadoop.

Problem Statement

In this Hadoop project, you will get to explore the numerous ways that can be used to solve the problem of small files in Hadoop.

What will you learn from this Hadoop Project?

  • Understand the problem with small files in greater detail through real-world cases where this inevitable situation can arise.

  • Learn several ways of overcoming the challenge in this project.

  • How small file problems in streaming can be resolved using a NoSQL database.

  • Using Flume to handle small files in streaming.

  • In-depth understanding of HDFS architecture

  • Introduction to Sequence files, Compression, CombineFileInput and their use in solving small problems in the Batch mode context

Access the solution to the Hadoop HDFS Project to deal with small file problem in Hadoop.

Datasets for Hadoop Projects with Code

This section contains sample Hadoop projects with source code that have been built using popular datasets.

4. Movielens dataset analysis using Hive for Movie Recommendations

Problem: The movielens dataset contains a large number of movies, with information regarding actors, ratings, duration, etc. We need to analyze this data and answer a few queries such as which movies were popular etc. 

Big data technologies used: Microsoft Azure, Azure Data Factory, Azure Databricks, Spark

Big Data Architecture: This sample Hadoop real-time project starts off by creating a resource group in azure. To this group, we add a storage account and move the raw data. Then we create and run an Azure data factory (ADF) pipelines. Following this, we spring up the Azure spark cluster to perform transformations on the data using Spark SQL. This makes the data ready for visualization that answers our analysis.

Movielens dataset analysis using Hive for Movie Recommendations

5. Million Song Dataset Challenge

This is a famous Kaggle competition for evaluating a music recommendation system. Users will work on the Million Song Dataset released by the Columbia University’s Lab for Recognition and Organization of Speech and Audio. The dataset consists of metadata and audio features for 1M contemporary and popular songs.

Problem Statement

For a given user, we have their song history and the count on how many times the song was played by a user.  In this big data project, we want to provide a set of recommendations to the user by understanding on how the song history can be used. This can be done by looking at songs which are most similar to user songs and also by grouping similar users based on their listening history. The challenging aspect of this big data hadoop project is to decide on what features need to be used to calculate the song similarity because there is lots of metadata for each song.

Learn to Manage and Schedule Hadoop Jobs with Oozie Workflow Scheduler by Working on Hands-On Oozie Projects

What will you learn from this hadoop project?

  • Learn to build a music recommendation system using Collaborative Filtering method.

  • Analysis large datasets easily and efficiently.

  • Using data flow programming language "Pig Latin" for analysis

  • Data compression using LZO codec

  • PigLatin UDF "DataFu" (Created by LinkedIn) for data localization

  • Working with Hierarchical Data Format (HDF5)

Access solution to the popular Kaggle Challenge- “Million Song Dataset

6. Yelp Dataset Analysis

Let us say today your family has decided that you are going vacay after the next two months. It is highly likely that the first thing you will do about it is, you will type your desired location in the google search box and look for things you can do in that city. What are the famous restaurants in the city, what all activities can you do, what are the kinds of shopping markets, etc. are the questions you will try to answer while browsing through numerous sites via that search. And, after you are done preparing the list, you will want to set your priorities and design the itinerary accordingly. 

And at this final, Yelp will help you. If your desired location is in the US or Canada, Yelp can help you prepare that final list as it offers crowd-sourced reviews about businesses through its website Yelp.com and the Yelp mobile app. Recently, in March 2021, Yelp shared its dataset to allow Data Science and Big Data enthusiasts worldwide to use their data and share insightful conclusions from it.

Problem Statement

In this Hadoop project, you can utilize the Yelp Dataset and implement data engineering techniques in Hadoop to learn storage, processing, and fetching datasets. 

Apply what you have learned, explore a variety of hands-on example projects for data engineers. 

What will you learn from this Hadoop Project?

  • What is Data Engineering

  • Utilizing the Yelp Dataset

  • Implementing Data Processing Tools

  • Benefits of choosing an online system over a batch system.

  • Learning how to apply data analytics and data mining techniques

Access the solution to the Hadoop Project for Beginners-Yelp Dataset Analysis

Enhance your data analytics knowledge with end-to-end solved big data analytics mini projects for final year students.

Apache Hadoop Projects for Beginners

This section has Hadoop live projects that are beginner-friendly. If you are specifically looking for Hadoop projects for students, this section will be helpful.

7. Recommendation System 

Recommendation System Hadoop Project

Business Use Case: The business use case here is to build one of the best recommender systems for an e-commerce site.

Objective and Summary of the project: Building recommendation systems is one of the most popular use cases that has been solved for e-commerce industries. With Apache Hadoop and Big Data now users get recommendations based on their previous searches. The technology majorly uses collaborative and context-based filtering to give accurate results based on browsing patterns.

Tools/Tech stack used: The tools and technologies used for building a recommendation system using Apache Hadoop are YouTube API, MapReduce, and Hive.

8. Cricket Match Analysis

Cricket Match Analysis Hadoop Project

Business Use Case: The business use case here is from the sports industry, where the aim is to predict results based on running performance.

Objective and Summary of the project: The sports industry is one of the most prominent industries in the world. This project uses historical and running data to come up with its predictions of a cricket match. Such predictive analysis uses various factors like player performance, team performance, stadium, and pitch, to name a few. With Hadoop, all these complex factors can easily be taken care of and predictions of a match can be done correctly.

Tools/Tech stack used: The tools and technologies used for such Cricket match predictions using Apache Hadoop are MapReduce and Hive

9. Airline Sentiment Analysis

Airline Sentiment Analysis Hadoop Project Example

Business Use Case: The business use case here is to perform sentiment analysis for the airline industry.

Objective and Summary of the project: Collecting feedbacks and reviews is a common way to understand a user’s experience based on the service given. The agenda of the above project is to gather reviews from Twitter or any other social media and analyze the sentiments of the feedback given. Such unstructured data has been easily handled by Apache Hadoop and with such mining of reviews now the airline industry targets the right area and improves on the feedback given.

Tools/Tech stack used: The tools and technologies used for such sentiment analysis using Apache Hadoop are Twitter, Twitter API, MapReduce, and Hive.

Intermediate Big Data Projects using Hadoop

This section has projects in Hadoop and Big Data that are suited for those have previous experience with big data algorithms and Apache Hadoop.

10. Wiki Page Ranking

Wiki Page Ranking Simple Hadoop Project

Business Use Case: The business use case here is to rank wiki pages using Hadoop.

Objective and Summary of the project: With so much data on the web today it becomes quite challenging to get the most accurate information based on the need. The main component of any search engine is the page ranking of each individual page of a website. With Apache Hadoop combined with machine learning algorithms now tons of pages online can be easily ranked for its web users.

Tools/Tech stack used: The tools and technologies used for such page ranking using Apache Hadoop are Linux OS, MySQL, and MapReduce.

11. Weblog Trend Analysis

Weblog Trend Analysis Hadoop Developer Project

Business Use Case: The business use case here is to do an analysis of weblogs and understand their trends.

Objective and Summary of the project: With the amount of data on the web today handling weblogs is one of the biggest challenges to solve. With Hadoop now the systems are designed in such a way that the response time of queries is reduced. With this weblogs can be tracked based on events and browsing patterns and histories.

Tools/Tech stack used: The tools and technologies used for such weblog trend analysis using Apache Hadoop are NoSql, MapReduce, and Hive.

12. Agricultural Data Analysis

Agricultural Data Analysis Hadoop Hive Project

Business Use Case: The business use case here is to get insights from data out of the agriculture industry.

Objective and Summary of the project: This project aims at helping the farmers of the world to increase the productivity of their work. This industry takes into consideration various factors like crop yield, weather forecast, and other crop details on a monthly or yearly basis. With Hadoop, all these factors have been able to give results to increase the productivity of crops and also work on the bottlenecks from time to time.

Tools/Tech stack used: The tools and technologies used for such agricultural data analysis using Apache Hadoop are MapReduce and Hive.

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Advanced Projects in Hadoop and Big Data

This section contains big data hadoop sample projects for professionals who excel in understanding how Apache Hadoop can be used for big data analysis and stream processing.

13. Text Analytics

Text Analytics Hadoop Apache Project

Business Use Case: The business use case here is to do text mining and extract relevant data from it.

Objective and Summary of the project: The main aim of the project is to do text analytics from a dataset of huge size. This is quite a cumbersome process but with Hadoop, this problem has been simplified. Now useful text can be comfortably located and clustered in different buckets. These buckets are further analyzed and acted upon to make corrective measures 

Tools/Tech stack used: The tools and technologies used for text mining projects using Apache Hadoop are Shell, HTML, PySpark, and Hue.

14. Document Analysis

Document Analysis Hadoop Project Idea

Business Use Case: The business use case here is to retrieve information from a document analysis application.

Objective and Summary of the project: When there is a huge amount of information in different languages and different sources it becomes quite difficult to retrieve insights from the same. With Hadoop and Pig platform one can achieve next-level extraction and interpretation of such complex unstructured data.

Tools/Tech stack used: The tools and technologies used for such document application analysis using Apache Hadoop are Pig, Mahout, and MapReduce.

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15. Performing SQL Analytics with Apache Hive

According to a ranking by DB-Engine, MySQL is the second most popular database in the world after Oracle. Followed by MySQL is the Microsoft SQL Server. This essentially suggests SQL is one of the most widely used databases. This popularity motivated the creation of Apache Hive for the Hadoop Ecosystem. Apache Hive is a data warehousing tool like SQL that allows SQL developers to effortlessly perform analytical methods on their Big Data. 

Problem Statement

In this Hadoop project, you will understand how to perform basic data analytics usually performed in SQL. But, instead of implementing data analytics techniques in SQL, you will be introduced to utilizing Apache Hive for that purpose.

What will you learn from this Hadoop Project?

  • What are Serializing and Deserializing and how do they function?

  • Building and executing a Scoop Job.

  • Transferring the data from MySQL to HDFS.

  • Making tables in Hadoop Hive and handling them.

  • Learning about Parquet and Xpath for accessing schema.

Access the solution to the Hive Project-Learn how to process and analyze big data using hive

16. Healthcare Data Management

Healthcare Data Management Hadoop Big Data Project

Business Use Case: The business use case here is to take care of sensitive data from the healthcare industry.

Objective and Summary of the project: Healthcare industries produce massive data every year and to do data management for this industry is one of their topmost priorities. Hadoop and other big data technologies have given a platform to the healthcare industry which is reliable and scalable at the same time. This system can even handle emergency situations if required.

Tools/Tech stack used: The tools and technologies used for such healthcare data management using Apache Hadoop are MapReduce and MongoDB.

Hadoop Banking Projects

This section will discuss the Hadoop projects for practice that belong to the finance industry.

17. Fraud Detection System

Business Use Case: A fraud detection system using Hadoop can be built to detect and prevent fraudulent activities in real-time. 

Objective and Summary of the Project: You can collect data in the form of transaction logs, user profiles, and payment gateway data and store it in HDFS. Clean the data and use feature engineering methods to ensure that the model has enough information to detect fraudulent activities. Build a machine learning model using Hadoop's machine learning libraries such as Mahout or Spark ML. Once the model is trained, deploy it to a Hadoop cluster and use it to detect fraud in real-time. Raise an alarming signal when frauds are detected.

18. Implementing OLAP on Hadoop using Apache Kylin

OLAP stands for Online Analytical Processing which is a method used by computer scientists to look for insights in data by analytically processing it. A multi-dimensional array of data that can be represented using a cube is called an OLAP cube. It is a data structure that promotes quick analysis of the dimensions that are vital for a business problem.

Problem Statement

In this Hadoop project, you can design an OLAP cube for the AdventureWorks database and build it using Apache Kylin. An additional exciting task can be to perform several queries on the cube and link familiar tools like MS Excel to the new cube.

What will you learn from this Hadoop Project?

  • What is Apache Kylin and how to use it?

  • Building star schema in Kylin

  • Designing queries for the Kylin cube

  • Create star schema on AdventureWorks dataset

  • Linking a data visualization tool to the cube

Access the solution to the Big Data Project using Apache Kylin for OLAP on Hadoop 

19. Bitcoin Data Mining

If you are an active user of Twitter, you can easily recall that the #Bitcoin started trending after a tweet by Elon Musk, one of the richest people on the planet, in June 2021. So what is this Bitcoin? Well, it is the world’s largest cryptocurrency, a currency that only exists electronically on computer systems. As of 14 June 2021, the value of one bitcoin is 3K USD, which is what makes it most exciting and worth exploring. 

Problem Statement

In this Hadoop project, you can analyze bitcoin data and implement a data pipeline through Amazon Web Services (AWS) Cloud. 

What will you learn from this Hadoop Project?

  • Extracting data from APIs using Python.

  • Uploading the data on HDFS.

  • Utilizing PySpark for reading data.

  • Implementing Kyro serialization technique and Spark optimization techniques.

  • Visualizing data through AWS Quicksight.

  • How to design tables using Hive/Pesto.

Access the solution to The Complete Guide to Start Mining Bitcoin in AWS cloud

Hadoop Projects for Resume

This section has Hadoop example projects that one can showcase in their resume to justify how well they understand the Hadoop concepts.

20. Log Analysis System

Business Use Case: A log analysis system using Hadoop is a powerful tool that can help organizations gain insights into their system and application logs. With Hadoop's distributed processing capabilities, the system can process large volumes of logs in real-time, identify patterns, and provide alerts when specific events occur.

Objective and Summary of the Project: The log analysis system can be built using Hadoop's MapReduce and HDFS features. Logs can be collected from different sources such as server logs, application logs, and network logs, and stored in HDFS. Hadoop's distributed processing capabilities can then be used to process the logs, analyze them, and generate insights.

21. Social Media Analytics 

Business Use Case: A social media analytics application can help businesses gain insights into their social media presence. With Hadoop's distributed processing capabilities, the system can process large volumes of social media data and provide insights into user behavior, sentiment analysis, and trending topics.

Objective and Summary of the Project: The social media analytics system can be built using Hadoop's MapReduce and HDFS features. Social media data can be collected from different sources such as Twitter, Facebook, and Instagram, and stored in HDFS. One of the key benefits of using Hadoop for social media analytics is its ability to handle large volumes of data. With social media generating a massive amount of data every day, Hadoop's scalability ensures that the system can handle this data and provide insights in real time.

Are you a beginner looking for Hadoop projects? Check out the ProjectPro repository with unique Hadoop Mini Projects with Source Code to help you grasp Hadoop basics.

Hadoop-based Academic Projects

This section contains big data projects using Hadoop that are popular in academia.

22. Genome Analytics

Business Use Case: By leveraging the power of Hadoop’s distributed computing and MapReduce, one can build a scalable and efficient solution for analyzing large genomic datasets, which can be useful for research, drug discovery, and healthcare applications.

Objective and Summary of the Project: The number of bases produced by modern sequencing techniques has increased exponentially as genome sequencing technology has undergone significant advancements.In this project, you will learn how MapReduce in Hadoop can be used for genome analysis, by running the WordCount class and an application called Hadoop-bam. You will also learn how Genome Analysis Toolkit (GATK) can be used to analyze large genome datasets, by providing a custom-built example. Read this paper on Genome Analysis with MapReduce to find out more about this project solution.

23. Energy Consumption Analysis

Business Use Case: To maximise energy use and cut expenses, utility companies and buildings must perform energy consumption analysis.  Hadoop can be used to build a scalable and efficient energy consumption analysis system.

Objective and Summary of the Project: The system can be built using Hadoop's distributed processing capabilities, and it can process large volumes of energy consumption data from smart meters, weather data, and other sources. The data can be ingested into HDFS, and then processed using Hadoop's MapReduce framework. You can use machine learning algorithms to identify patterns in the energy consumption dataset and visualize the results using a data visualization tool such as Microsoft Power BI or Tableau.

24. Cybersecurity Threat Analysis

Business Use Case: Enhancing incident response, improving incident detection, and how these events affect your organisation are the three main focuses of cybersecurity. Because Hadoop is built to give access to analytics, contextual knowledge, and information, all three of these topics are possible.

Objective and Summary of the Project: Hadoop can be used to build a cybersecurity threat analysis system that can ingest large volumes of log data from network devices and servers to identify potential threats. The system can use machine learning algorithms to detect anomalies and identify patterns in network traffic and detect potential threats. This project can be useful for cybersecurity companies and IT departments.

25. Traffic Congestion Analysis

Business Use Case: Slower speeds, longer travel times, and more vehicles queuing up are signs of traffic congestion on road networks. We can all see that the number of vehicles is rising dramatically every day, yet the road system is not keeping up. It causes traffic congestion to worsen over time. To identify traffic congestion and improve the effectiveness of congestion management, various technologies are deployed. In this project idea, the goal is to build a Hadoop-based traffic management system that can analyze traffic congestion after ingesting traffic data from various sources such as traffic cameras, GPS, and road sensors.  

Objective and Summary of the Project: A Radio Frequency Identification technique (RFID) can be used to collect data for controlling traffic congestion.  The data derived from it needs to be processed before being used by the various roadside traffic control systems. Due to the enormous data volumes, processing the data could be problematic. As a result, using the Hadoop Architecture for solving this issue will be a good way out. As Map Reduce design leverages the parallel processing paradigm at its heart, the data can be made available at numerous sites by using the Map Reduce framework. The objective is to put in place a system that would track the route taken by specific vehicles as they pass the roadside readers, calculate an average trip duration, and then make that data available to the various toll centres.

26. Hadoop Project: Performing COVID-19 Analysis through PySpark and Apache Hive

As the coronavirus drastically hit the world and was declared as a pandemic in 2020 by the World Health Organisation, countries worldwide decided to enforce a complete lockdown to limit the transmission of the virus. And as not all countries are able to vaccinate their 100% population, analyzing the collected COVID-19 data has become extremely important to help the governments design their lockdown policy.

Covid-19 Analysis Hadoop Mini Project with Source Code

Problem Statement

In this Hadoop project, you will get to understand how to perform data analytics like a Big Data Professional in the industry. 

You will be introduced to exciting Big Data Tools like AWS, Kafka, NiFi, HDFS, PySpark, and Tableau.

What will you learn from this Hadoop Project?

  • Implementing a Big Data project on AWS.

  • Understanding how NiFi is used for the real-time streaming data import from external API

  • Import data from Kafka and process it before storing it in HDFS.

  • Making tables in Hadoop Hive and performing queries over them.

  • Visualizing the given data using Tableau and AWS Quicksight.

Access the solution to the PySpark Project- Create a data pipeline using Spark and Hive - Covid-19 Analysis.

Hadoop Telecommunication Projects

27. Facebook Data Analysis

Facebook Data Analysis Simple Hadoop Project

Business Use Case: The business use case here is to analyze various types of data that are generated on Facebook.

Objective and Summary of the project: With social media sites gaining popularity, it has become quite crucial to handle the security and pattern of various data types of the application. The use of Facebook or something similar is at every home around the globe, thus producing tons of data. With Apache Hadoop and associated tools, all unstructured and structured data produced by such sites can be reliably stored, processed, and maintained.

Tools/Tech stack used: The tools and technologies used for such Facebook data analysis using Apache Hadoop are Facebook API, MapReduce, and Hive.

28. Searching Unique URLs

This is an interesting Hadoop Project for beginners who want to familiarize themselves with the art of performing data queries and analytics using Apache Hive. Hive supports an SQL-like interface for retrieving data from several databases and file systems that blend with Hadoop. If you are comfortable with SQL, then this project will be easy-peasy for you.

Problem Statement

In this Hadoop project, you can explore how to fetch the first unique URL from a file containing 200 billion records for URLs.

What will you learn from this Hadoop Project?

  • What is Apache Hive and how it is implemented in the real world?

  • Building programs in Hive.

  • Understanding Hadoop MapReduce component.

  • Creating external tables.

Access the solution to the Hadoop Projects for Beginners-Learn to write a Hive program

Big Data Hadoop Projects with Source Code on GitHub

This section has Hadoop example projects along with GitHub repository links.

29. Hadoop-Based Deep Learning

This project uses Hadoop for large-scale deep learning tasks. It provides a distributed computing environment for running deep learning algorithms on large datasets. The project leverages technologies like TensorFlow, Caffe, and PyTorch for training deep neural networks, and Hadoop for distributed storage and computation.

Repository Link: https://github.com/tensorflow/ecosystem/tree/master/hadoop 

30. Luigi 

Luigi is an open-source Python module for building data pipelines on Hadoop, allowing for scalable, distributed processing of large datasets. The project provides an easy-to-use interface for defining tasks and dependencies, allowing developers to build complex data workflows using simple Python code. Luigi supports various Hadoop ecosystem tools such as HDFS, Hive, and Pig, making it a powerful tool for big data processing. It also supports parallel processing and scheduling, allowing for efficient use of computing resources. With its user-friendly interface and powerful capabilities, Luigi is an ideal solution for managing data workflows on Hadoop clusters.

Repository Link: https://github.com/spotify/luigi 

31. Hadoop-Cluster-in-Machine-Learning

This project on GitHub provides a step-by-step guide on how to set up a Hadoop cluster for machine learning. The project covers the installation of Hadoop on a single machine, configuring the cluster, and using Hadoop for large-scale data processing. It also includes examples of machine learning algorithms that can be implemented on the Hadoop cluster, such as decision trees and clustering. The project aims to provide an accessible way for beginners to learn about Hadoop and machine learning, as well as provide a framework for scalable data processing and analysis.

Repository Link: https://github.com/MayurakshaSikdar/Hadoop-Cluster-in-Machine-Learning 

Hadoop Open-Source Projects

In this section, we will discuss popular Hadoop projects that are open source and you can contribute to them.

32. Pig

Apache Pig is a high-level data flow language and execution framework for parallel computation on Hadoop clusters. Pig provides a simple and flexible scripting language, enabling users to easily perform data analysis tasks and build complex workflows.

Repository Link: https://github.com/apache/pig 

33. Hive

Apache Hive is a data warehousing solution for Hadoop that allows users to query and analyze large datasets using SQL-like commands. Hive also supports custom MapReduce scripts, making it a flexible and scalable solution for data processing and analytics in Hadoop.

Repository Link: https://github.com/apache/hive 

34. Hbase

Apache HBase is a distributed, non-relational database built on top of Hadoop, providing fast and scalable storage for structured data. With support for random read and write operations, HBase is ideal for applications that require low-latency access to large datasets.

Repository Link: https://github.com/apache/hbase

35. Mahout

Apache Mahout is a scalable machine learning and data mining library built on top of Hadoop, providing a suite of algorithms for clustering, classification, and recommendation tasks. With support for large-scale data processing, Mahout is ideal for applications that require sophisticated analysis of big data. 

Repository Link: https://github.com/apache/mahout 

36. Storm

Apache Storm is a distributed stream processing system built on top of Hadoop, providing real-time processing of high-velocity data streams. With support for parallel computation and fault-tolerance, Storm is ideal for applications that require fast and reliable processing of continuous data streams.

Repository Link: https://github.com/apache/storm 

Use ProjectPro to Learn Hadoop with Live Projects!

The collection of these projects on Hadoop and Spark will help professionals master the big data and Hadoop ecosystem concepts learnt during their hadoop training. The changed paradigm, increasing demand and competition requires Hadoop developers to be very strong at applying Hadoop concepts in practicality. Hadoop projects for beginners are simply the best thing to do to learn the implementation of big data technologies like Hadoop. Building a project portfolio will not merely serve as a tool for hiring managers but also will boost your confidence on being able to speak about real hadoop projects that you have actually worked on. Having multiple hadoop projects on your resume will help employers substantiate that you can learn any new big data skills and apply them to real life challenging problems instead of just listing a pile of hadoop certifications.

"Hadoop created this centre of gravity for a new data architecture to emerge. Hadoop has this ecosystem of interesting projects that have grown up around it."-  said Shaun Connolly, VP of corporate strategy at Hadoop distribution company Hortonworks.

“What are some interesting beginner level big data hadoop projects that I can work on to build my project portfolio?” – This is one of the most common question asked by students who complete Hadoop Training and Certification from ProjectPro. There are various kinds of hadoop projects that professionals can choose to work on which can be around data collection and aggregation, data processing, data transformation or visualization.

ProjectPro has collated a list of major big data projects within the Hadoop and spark ecosystem that will help professionals learn on how to weave these big data technologies together in production deployments. Working on these Hadoop projects will not just help professionals master the nuances of Hadoop and Spark technologies but understand how they actually help solve real world challenges and how various companies use them. These Hadoop projects come with detailed understanding of the problem statement, source code, dataset and a video tutorial explaining the entire solution. You can always rely on these Hadoop projects and make the best use of available time and resources to master the Hadoop ecosystem that will help you fetch your next best Hadoop developer job.

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FAQs

What are the challenges that faced when implementing Hadoop projects?

Some of the challenges faced when implementing Hadoop projects include managing large and complex datasets, ensuring data quality and security, optimizing performance, and integrating Hadoop with existing systems and tools. Additionally, there may be a need for specialized skills and resources to effectively design, develop, and maintain Hadoop clusters and applications.

What is Hadoop best used for?

Hadoop is best used for storing, processing, and analyzing large volumes of structured and unstructured data, especially for tasks that require distributed computing across clusters of commodity hardware. It is commonly used for big data analytics, machine learning, and other data-intensive applications.

What is the percentage of failed Hadoop projects?

The percentage of failed Hadoop projects varies widely depending on the specific project and its implementation. However, a report by Gartner suggests that the failure rate can range from 50% to as high as 85%.

 

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