The Big Data and Hadoop training course from Edureka is designed to enhance your knowledge and skills to become a successful Hadoop developer.

In-depth knowledge of core concepts will be covered in the course along with implementation on varied industry use-cases.

Course Objectives
1. Master the concepts of HDFS and MapReduce framework
2. Understand Hadoop 2.x Architecture
3. Setup Hadoop Cluster and write Complex MapReduce programs
4. Learn data loading techniques using Sqoop and Flume
5. Perform data analytics using Pig, Hive and YARN
6. Implement HBase and MapReduce integration
7. Implement Advanced Usage and Indexing
8. Schedule jobs using Oozie
9. Implement best practices for Hadoop development
10. Work on a real life Project on Big Data Analytics

This Course is for:
1. Analytics professionals
2. BI /ETL/DW professionals
3. Project managers
4. Testing professionals
5. Mainframe professionals
6. Software developers and architects
7. Graduates aiming to build a successful career around Big Data

Course Curriculum:
1. Understanding Big Data and Hadoop
Topics -
Big Data, Limitations and Solutions of existing Data Analytics Architecture, Hadoop, Hadoop Features, Hadoop Ecosystem, Hadoop 2.x core components, Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework, Anatomy of File Write and Read, Rack Awareness.

2. Hadoop Architecture and HDFS
Topics -
Hadoop 2.x Cluster Architecture - Federation and High Availability, A Typical Production Hadoop Cluster, Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Password-Less SSH, MapReduce Job Execution, Data Loading Techniques: Hadoop Copy Commands, FLUME, SQOOP.

3. Hadoop MapReduce Framework - I
Topics -
MapReduce Use Cases, Traditional way Vs MapReduce way, Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components, YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program, Demo on MapReduce.

4. Hadoop MapReduce Framework - II
Topics -
Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce Job Submission Flow, Demo of Input Splits, MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set.

5. Advanced MapReduce
Topics -
Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format.

6. Pig
Topics -
About Pig, MapReduce Vs Pig, Pig Use Cases, Programming Structure in Pig, Pig Running Modes, Pig components, Pig Execution, Pig Latin Program, Data Models in Pig, Pig Data Types.
Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF, Pig Demo on Healthcare Data set.

7. Hive
Topics -
Hive Background, Hive Use Case, About Hive, Hive Vs Pig, Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database, Hive Data Types and Data Models, Partitions and Buckets, Hive Tables(Managed Tables and External Tables), Importing Data, Querying Data, Managing Outputs, Hive Script, Hive UDF, Hive Demo on Healthcare Data set.

8. Advanced Hive and HBase
Topics -
Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive : Thrift Server, User Defined Functions.
HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, HBase Cluster Deployment.

9. Advanced HBase
Topics -
HBase Data Model, HBase Shell, HBase Client API, Data Loading Techniques, ZooKeeper Data Model, Zookeeper Service, Zookeeper, Demos on Bulk Loading, Getting and Inserting Data, Filters in HBase.

10. Oozie and Hadoop Project
Topics -
Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web Console, Hadoop Project Demo.