Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Hadoop Cluster- Single and Multi node, Hadoop 2.x, Flume, Sqoop, Map-Reduce, PIG, Hive, Hbase, Zookeeper, Oozie etc. will be covered in the course. This course is designed for professionals aspiring to make a career in Big Data Analytics using Hadoop Framework. Software Professionals, Analytics Professionals, ETL developers, Project Managers, Testing Professionals are the key beneficiaries of this course. Other professionals who are looking forward to acquire a solid foundation of Hadoop Architecture can also opt for this course.

UNDERSTANDING BIG DATA AND HADOOP

  • Understand What Is Big Data
  • Analyze Limitations And Solutions Of Existing Data Analytics Architecture
  • Understand What Is Hadoop And Its Features
  • Hadoop Ecosystem
  • Understand Hadoop 2.x Components
  • Perform Read And Write In Hadoop
  • Understand Rack Awareness Concept

HADOOP ARCHITECTURE AND HDFS

  • Run Hadoop In Different Cluster Nodes
  • Implement Basic Hadoop Commands On Terminal
  • Prepare Hadoop 2 Configuration Files Analyze The Parameters In It.
  • Implement Password-less Ssh On Hadoop Cluster
  • Analyze Dump Of A Mapreduce Program
  • Implement Different Data Loading Techniques

HADOOP MAPREDUCE FRAMEWORK – I

  • Analyze Different Use-cases Where Mapreduce Is Used
  • Differentiate Between Traditional Way And Mapreduce Way
  • Learn About Hadoop 2.x Mapreduce Architecture And Components
  • Understand Execution Flow Of Yarn Mapreduce Application
  • Implement Basic Mapreduce Concepts
  • Run A Mapreduce Program

HADOOP MAPREDUCE FRAMEWORK – II

  • Analyze Mapreduce Job Submission Flow
  • Implement Combiner And Partitioner In Mapreduce
  • Understand Mapreduce Codes In Details
  • Code In Mapreduce For A Given Problem Statement
  • Understand Input Splits Concepts In Mapreduce
  • Module 5– Advance Mapreduce
  • Implement Counter In Mapreduce
  • Numerical Summarizations
  • Counting With Counters
  • Top K Records
  • Distinct Records
  • Total Order Sorting
  • Reduce Side Join
  • Replicated Join
  • Implement Distributed Cache Concept In Mapreduce
  • Customizing Input And Output In Hadoop
  • Implement Custom Input Format In Mapreduce
  • Implement Sequence Input Format In Mapreduce
  • Implement Xml Input Format In Mapreduce

INTRODUCTION TO CLOUDERA AND UNDERSTANDING PIG

  • Pig And Its Need
  • Difference Between Pig And Mapreduce
  • Pig Features And Programming Structure
  • Pig Running Modes
  • Pig Components And Data Models
  • Basics Operations In Pig
  • Udf In Pig

UNDERSTANDING HIVE

  • Hive And Its Use Cases
  • Hive Vs. Pig
  • Hive Architecture And Components
  • Primitive And Complex Type In Hive
  • Data Models In Hive
  • Query Efficiency Measures
  • Partitioning
  • Bucketing
  • Hive Script And Hive Udf

UNDERSTANDING SQOOP AND FLUME

  • Implement Flume Job To Download Data From Twitter
  • Implement Flume Job To Download Data From Other Sources
  • Implement Sqoop To Import Table From Rdbms Into Hdfs.
  • Implement Sqoop To Import All Tables From Rdbms Into Hdfs.
  • Implement Sqoop To Import Table From Rdbms Into Hive.
  • Implement Sqoop To Import Schema And Tables Details Rdbms.
  • Implement Sqoop To Export Data To Rdbms (insert And Update Mode)
  • Implement Sqoop To Generate Java Classes Which Encapsulate And Interpret Imported Records

INTRODUCTION TO NOSQL AND WORKING WITH OOZIE

  • Understand Oozie
  • Schedule Job In Oozie
  • Implement Oozie Workflow
  • Implement Oozie Coordinator

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