Learn Apache Hadoop basics with MapReduce programming

Table of Contents

1. Introduction
2. Setting up MapReduce and Apache Hadoop in IntelliJ
3. Creating a New Maven Project
4. Importing Dependencies for Hadoop
5. Creating Project Files
6. Copying and Pasting Word Count Code
7. Adding Configuration and Arguments
8. Creating an Input Directory and File
9. Running the MapReduce Program
10. Review and Conclusion

1. Introduction

Welcome to this guide on getting started with setting up MapReduce and Apache Hadoop inside of IntelliJ. In this article, we will walk You through the necessary steps to begin writing MapReduce code and performing data analytics tasks.

2. Setting up MapReduce and Apache Hadoop in IntelliJ

To begin, open IntelliJ and Create a new project. Make sure to select Java 1.8 as the language version, and choose Maven as the project Type.

3. Creating a New Maven Project

In this step, we will create a new Maven project to work with. Choose a proper location for your project and give it a suitable name.

4. Importing Dependencies for Hadoop

In order to work with Hadoop, we need to import the necessary dependencies. This includes adding the Apache Hadoop repository and specifying the required Artifact IDs and versions.

5. Creating Project Files

Once the dependencies are resolved, we can start creating our project files. We will be copying and pasting the Word Count code from the official Apache Hadoop documentation page.

6. Copying and Pasting Word Count Code

In this step, we will copy the basic Word Count code from the Apache Hadoop documentation and paste it into our project directory. We will create a new Java class with the filename "WordCount".

7. Adding Configuration and Arguments

To configure our Word Count program, we need to add a configuration file. After running the program, we will specify the input and output directory paths as program arguments.

8. Creating an Input Directory and File

In order for the Word Count program to work, we need to create an input directory and a file inside it. We can generate random words using websites like lipsum.com and save them in the input file.

9. Running the MapReduce Program

With the input and output paths specified, we can now run the MapReduce program. The program will process the files in the input directory and provide the word count as output.

10. Review and Conclusion

Congratulations! You have successfully set up MapReduce and Apache Hadoop in IntelliJ and run your first MapReduce program. In the next video, we will dive deeper into the syntax and functionality of MapReduce code.

Article:

Getting Started with Setting up MapReduce and Apache Hadoop in IntelliJ

Welcome to this guide on getting started with setting up MapReduce and Apache Hadoop inside of IntelliJ. In this article, we will walk you through the necessary steps to begin writing MapReduce code and performing data analytics tasks.

To begin, open IntelliJ and create a new project. Make sure to select Java 1.8 as the language version, and choose Maven as the project type. Once you have created the project, import the necessary dependencies for Hadoop. This includes adding the Apache Hadoop repository and specifying the required artifact IDs and versions.

After importing the dependencies, you can start creating your project files. Copy the basic Word Count code from the official Apache Hadoop documentation page and paste it into a new Java class called "WordCount".

Next, you need to configure your Word Count program by adding a configuration file. After running the program, specify the input and output directory paths as program arguments.

To provide input for the Word Count program, create an input directory and a file inside it. You can use websites like lipsum.com to generate random words and save them in the input file.

With the input and output paths specified, you are ready to run the MapReduce program. The program will process the files in the input directory and provide the word count as output.

Congratulations on successfully setting up MapReduce and Apache Hadoop in IntelliJ and running your first MapReduce program. Stay tuned for the next video where we will explore the syntax and functionality of MapReduce code in more Detail.