This document outlines the content of a course on BIG DATA & HADOOP. The course is intended for developers and data warehouse professionals and will provide both lectures and hands-on experience working with Hadoop. It will cover key concepts like HDFS, MapReduce, YARN, and popular Hadoop ecosystems including Hive, Pig, HBase, Spark, and Impala. Lectures will be complemented by assignments and exercises working directly in a local virtual Hadoop cluster to gain experience administering, configuring, and troubleshooting Hadoop systems.

1. BIG DATA & HADOOP

2. Introduction
• What is big data?
• What is the need for big data in present corporate
world?
• What are the objectives covered in the big data?
(Brief on the objectives)
• How is big data useful to learners ?
• What are the prerequisites of Big data?
• How is the current Big Data Market in terms of Jobs
opportunities to participants ?

3. Course Theme
• This course is intended for Developers and Data
Warehouse professional
• The course touches administration flavour
• Data visualization using Tableau will be show cased
• Entire course runs through use case oreinted
• All hands-on shall be done in your local virtual
machines
• The Course consists 50:50 splits between lectures
and hands-on

4. Course Content
Part 1 (Understanding Big Data Concepts)
1. Big Data Challenges
2. Introduction to Hadoop
3. Introduction to Hadoop eco system
4. Type of use cases
5. Basic Unix/Linux commands
6. Basic Java refresher

5. Course Content
Part 2 (Introduction)
1. Concepts of distributed file system
2. Hadoop distributed file system (HDFS)
3. Hadoop core daemons
4. Hadoop Map Reduce Framework
5. Job scheduling logic
6. In-depth of Mappers, Reducers, sorting and shuffling
7. File read and write semantics
8. Hands-on & Assignments

6. Course Content
Part 3 (Hadoop 2.0)
1. YARN
2. Name node high availability
3. HDFS Federation
4. Resource Manager
5. Node Manager
6. App Master

7. Course Content
Part 4 (Classic Hadoop Eco systems)
1. Apache Hive - Warehouse,
2. Apache Pig - ETL
3. Apache HBase - DataStore
4. Apache Scoop - Data Integrator (RDBMS)

8. Course Content
Part 5 (Advanced Hadoop Eco systems)
1. Apache Tez
2. Apache Spark
3. Spark SQL , Mlib and Graphx
4. Spark Streaming
5. Cloudera Impala
6. Apache Hue

9. Course Content
Part 6 (Hadoop Administration & Configuration)
1. Apache Ambari (Administration tool)
2. Apache ZooKeeper (Configuration Management)
3. Hardware and Software considerations
4. Monitoring Hadoop daemons and ports
5. Understanding system logs
6. Trouble shooting cluster problems