Turn into a Hadoop Expert by getting in depth of MapReduce, Yarn, Pig, hive, HBase, Oozie, Flume and Sqoop while dealing with industry-based Use-cases and Projects. Big Data and Hadoop instructional class is intended to give information and aptitudes to end up a fruitful Hadoop Developer. Inside and out information of center ideas will be canvassed in the course alongside execution on shifted industry utilize cases.

Why Learn Big Data and Hadoop?

The world is getting progressively advanced, and this implies enormous information is setting down deep roots. Actually, the significance of enormous information and information examination will keep developing in the coming years. Picking a profession in the field of enormous information and examination may very well be the kind of job that you have been attempting to discover to live up to your vocation desires. As an ever-increasing number of organizations understand the requirement for experts in enormous information and examination, the quantity of these employments will keep on developing. Near 80% of information researchers say there is right now a deficiency of experts working in the field.

How Big is this Big Data?

  • Definition with Real Time Examples
  • How Bigdata is produced with Real Time Generation
  • Use of Bigdata-How Industry is using Bigdata
  • Traditional Data Processing Technologies
  • Future of Bigdata!!!
  • Why Hadoop?
  • What is Hadoop?
  • Hadoop versus RDBMS, Hadoop versus Bigdata
  • Brief history of Hadoop
  • Apache Hadoop Architecture
  • Problems with customary substantial scale frameworks
  • Requirements for another methodology
  • Anatomy of a Hadoop group
  • Hadoop Setup and Installation.
  • Brief Introduction about Hadoop Ecosystem (MapReduce, HDFS, Hive, PIG, HBase).
  • Concepts and Architecture
  • Data Flow (File Read, File Write)
  • Fault Tolerance
  • Shell Commands
  • Java Base API
  • Data Flow Archives
  • Coherency
  • Data Integrity
  • Role of Secondary Name Node
  • HDFS Programming Basics
  • MapReduce Architecture
  • Data Flow (Map – Shuffle – Reduce)
  • MapRed versus MapReduce APIs
  • MapReduce Programming Basics
  • Programming [ Mapper, Reducer, Combiner, Partitioner ]
  • Architecture
  • Installation
  • Configuration
  • Hive versus RDBMS
  • Tables
  • DDL and DML
  • Partitioning and Bucketing
  • Hive Web Interface
  • Why Pig
  • Use instance of Pig
  • HBase Introduction

Length: The term of this workshop will be two successive days.


  • Introduction with Industry Experts.
  • Hands on Practice.
  • Declaration of Participation by Hack7.

Workshop information:-

Duration: 2 days.