Hadoop Project

Project Overview

This project is designed to leverage Apache Hadoop for big data processing and analysis. It provides a framework for distributed storage and processing of large datasets across clusters of computers using simple programming models.

What This Project Can Do

• Data Processing: Efficiently process large datasets using Hadoop's MapReduce framework.
• Data Storage: Store vast amounts of data in a distributed file system (HDFS).
• Scalability: Scale horizontally by adding more nodes to the cluster.
• Fault Tolerance: Ensure high availability of data with built-in redundancy.

Requirements

Before you begin, ensure you have met the following requirements:

• Java Development Kit (JDK) version 8 or higher
• Apache Hadoop version 3.x installed
• A configured Hadoop cluster or a single-node setup
• Appropriate access permissions for HDFS

Installation

1. Install Java:

   • Make sure you have Java installed. You can check by running:

     java -version

   • If Java is not installed, you can download and install it from Oracle's official website.

2. Install Apache Hadoop:

   • Download Hadoop from the Apache Hadoop releases page.

   • Extract the downloaded archive:

     tar -xzf hadoop-x.x.x.tar.gz

   • Move it to your desired installation directory:

     mv hadoop-x.x.x /usr/local/hadoop

   • Add Hadoop to your PATH by editing your .bashrc or .zshrc file:

     export HADOOP_HOME=/usr/local/hadoop
     export PATH=$PATH:$HADOOP_HOME/bin

3. Configure Hadoop:

   • Edit the configuration files located in $HADOOP_HOME/etc/hadoop to suit your setup. Key files include:

     • core-site.xml
     • hdfs-site.xml
     • mapred-site.xml
     • yarn-site.xml

   • Start the Hadoop services:

     start-dfs.sh
     start-yarn.sh

Running the Project

1. Upload Data to HDFS:

   • Use the following command to upload a file to HDFS:

     hdfs dfs -put /local/path/to/your/data.txt /hdfs/path/to/data.txt

2. Execute MapReduce Job:

   • To run a MapReduce job, use the following command:

     yarn jar /path/to/your/hadoop-project.jar com.example.YourMainClass /hdfs/path/to/data.txt /hdfs/output/path

3. View Output:

   • After the job completes, you can view the output stored in HDFS:

     hdfs dfs -cat /hdfs/output/path/part-r-00000

Project:

Project1: Java classes for word counting, including drivers, mappers, and reducers. It processes beer reviews from a CSV file, generates word counts, and outputs results in a designated directory. The project is executed via JAR files using Hadoop's command-line interface.