All Programs of the 8th Sem BDA Lab with Outputs in Different Steps. This document contains all practical programs of the Big Data Analytics Lab, covering major concepts such as Hadoop setup, HDFS operations, MapReduce programming, Pig, Hive, and real-life data analytics. Each experiment includes step-by-step execution and output to help students understand distributed data processing. These programs focus on core phases of big data processing such as data storage, data distribution, parallel computation, data mining, and analytics using Hadoop ecosystem tools.

The Big Data Analytics Lab as per the curriculum of Rajasthan Technical University (RTU), is a practical-oriented course designed to provide hands-on experience in handling, processing, and analyzing large-scale datasets using modern big data technologies. This lab complements theoretical knowledge by enabling students to implement distributed computing solutions and work with real-world datasets.

The primary objective of this lab is to familiarize students with big data tools and frameworks and develop practical skills for large-scale data analysis.

1. Understand the fundamentals of big data systems.
2. Learn Hadoop installation and configuration.
3. Implement distributed file management.
4. Develop MapReduce programs.
5. Use Pig and Hive for data analysis.
6. Analyze real-life datasets.

The lab covers a wide range of The BDA lab topics including:

• Java-based data structure implementation.
• Hadoop installation and configuration.
• HDFS file operations.
• MapReduce programming.
• Data mining using Pig and Hive.
• Large-scale matrix and weather data analysis.
• Real-world big data problem solving.

1. Data Structures in Java
2. Hadoop Setup and Configuration
3. HDFS File Management
4. MapReduce Programming
5. Pig and Hive Analytics
6. Big Data Case Studies

1. Programming Language: Java
2. Big Data Framework: Hadoop
3. Data Processing Model: MapReduce
4. Query Tools: Pig, Hive
5. Platform: Linux / Windows
6. Development Environment: Command Line, Eclipse/IntelliJ (optional)

1. Data Structure Programs in Java
2. Hadoop Installation and Configuration
3. HDFS File Operations
4. MapReduce Applications
5. Pig and Hive Based Data Analysis
6. Mini Project on Real-Life Big Data Problem

1. Assignments: 5%
2. Projects & Implementation Lab Programs: 50%
3. Midterm Exam: 10%
4. Final Exam: 35%

After completing this lab, students will be able to:

• Implement basic and advanced data structures in Java.
• Install and configure Hadoop clusters in different modes.
• Perform file management & operations in HDFS.
• Develop MapReduce programs for data analysis.
• Use Pig and Hive for structured data processing, semi-structured data processing and analytics.
• Process large-scale datasets efficiently.
• Apply big data techniques to real-world problems.

• Implementing Linked List, Stack, Queue, Set, and Map in Java.
• Setting up Hadoop in Standalone, Pseudo-distributed, and Fully Distributed modes.
• Performing file operations in HDFS.
• Writing Word Count MapReduce program.
• Mining weather data using MapReduce.
• Implementing Matrix Multiplication using Hadoop.
• Writing Pig Latin scripts for sorting, grouping, and joining data.
• Creating and managing databases and tables using Hive.
• Solving real-life big data case studies.

The BDA Lab bridges the gap between theoretical concepts and real-world big data applications. It prepares students for careers in data engineering, data analytics, and distributed systems by providing practical exposure to industry-standard tools and techniques.

1. Introduction: Objective, scope, and outcomes of the course & Installation of VMWare.

2. Implement the following Data Structures in Java: i) Linked Lists ii) Stacks iii) Queues iv) Set v) Map

3. Perform setup and installation of Hadoop in: i) Standalone Mode ii) Pseudo-Distributed Mode iii) Fully Distributed Mode

4. Implement file management tasks in Hadoop: i) Adding files and directories ii) Retrieving files iii) Deleting files

5. Run a basic Word Count MapReduce program to understand the MapReduce paradigm.

6. Write a Map Reduce program that mines weather data. Weather sensors collecting data everyhour at many locations across the globe gather a large volume of log data, which is a goodcandidate for analysis with MapReduce, since it is semi structured and record-oriented.

7. Implement Matrix Multiplication using Hadoop MapReduce.

8. Write a Map Reduce program that mines weather data. Weather sensors collecting data everyhour at many locations across the globe gather a large volume of log data, which is a goodcandidate for analysis with MapReduce, since it is semi structured and record-oriented.

9. Install and Run Hive then use Hive to create, alter, and drop databases, tables, views, functions, and indexes.

10. Solve some real life big data problems.