Hadoop Development Development


About the Course

Big Data Hadoop online training is designed to help you become a top Hadoop developer. During this course, our expert instructors will help you:
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
11. Understand Spark and its Ecosystem
12. Learn how to work in RDD in Spark
 

Curriculum

1. Understanding Big Data and Hadoop

Learning Objectives - In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, how MapReduce Framework works. 

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, Hadoop Different Distributions.
2. Hadoop Architecture and HDFS

Learning Objectives - In this module, you will learn the Hadoop Cluster Architecture, Important Configuration files in a Hadoop Cluster, Data Loading Techniques, how to setup single node and multi node hadoop cluster. 

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, Single node cluster and Multi node cluster set up Hadoop Administration.
3. Hadoop MapReduce Framework

Learning Objectives - In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets. 

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. Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set.
4. Advanced MapReduce

Learning Objectives - In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing. 

Topics - Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format, Xml file Parsing using MapReduce.
5. Pig

Learning Objectives - In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, PIG running modes, PIG UDF, Pig Streaming, Testing PIG Scripts. Demo on healthcare dataset. 

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, Shell and Utility Commands, Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Specialized joins in Pig, Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function, Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution ), Pig Streaming, Testing Pig scripts with Punit, Aviation use case in PIG, Pig Demo on Healthcare Data set.
 

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Certification

Certification