BigData /
Hadoop

Hadoop

Hadoop Online Training

What is Hadoop ?

1) Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment.
2) It is part of the Apache project sponsored by the Apache Software Foundation.
3) The process of taking a Hadoop project from conception to completion.
4) Big Data defined and how Hadoop makes Big Data more valuable.
5) MapReduce training: Including how to write a MapReduce program using the Hadoop API.
6) HDFS (Hadoop Distributed File System) training: Including effective loading and processing of data with CLI and API.
7) Pig, Hive and Hcatalog: how to accomplish Data movement and processing with higher level languages.
8) Over 15 hands-on training exercises using HDFS, Pig, Hive, HBase, key MapReduce components and features (e.g. mapper, reducer, combiner, partitioner and streaming) and more.

Who is Eligible:

Any Graduates/Post Graduates are eligible to learn HADOOP OnlineTraining.
Anyone having knowledge in Data Warehousing technologies.
Freshers/Exp people looking for complete Big Data training.
Java skilled people, and basic unix knowledge is also required.
Any technical background consultants are eligible to learn “Hadoop Online Training”.

  • 1

    What is Hadoop?

    20:56
  • 2

    History of Hadoop

    20:56
  • 3

    Hadoop Basic Concepts

    20:56
  • 4

    Building Blocks – Hadoop Eco-System

    20:56
  • 5

    Who is behind Hadoop?

    20:56
  • 6

    What Hadoop is good for and why it is Good

    20:56
  • 1

    Configuring HDFS

    20:56
  • 2

    Interacting With HDFS

    20:56
  • 3

    HDFS Permissions and Security

    20:56
  • 4

    Additional HDFS Tasks

    20:56
  • 5

    HDFS Overview and Architecture

    20:56
  • 6

    HDFS Installation

    20:56
  • 7

    Hadoop File System Shell

    20:56
  • 8

    File System Java API

    20:56
  • 1

    High Availability

    20:56
  • 1

    Big Data opportunities

    20:56
  • 2

    Big Data Challenges

    20:56
  • 1

    Introduction to Pig

    20:56
  • 2

    Introduction to Hive

    20:56
  • 3

    Introduction to HBase

    20:56
  • 4

    Other eco system Map

    20:56
  • 1

    Hadoop Installation & Configuration

    20:56
  • 2

    Setting up Standalone system

    20:56
  • 3

    Setting up pseudo distributed cluster

    20:56
  • 4

    Setting up distributed cluster

    20:56
  • 1

    Map and Reduce Basics.

    20:56
  • 2

    How Map Reduce Works

    20:56
  • 3

    Anatomy of a Map Reduce Job Run

    20:56
  • 4

    Job Submission, Job Initialization, Task Assignment, Task Execution

    20:56
  • 5

    Progress and Status Updates

    20:56
  • 6

    Job Completion, Failures

    20:56
  • 7

    Shuffling and Sorting.

    20:56
  • 9

    Hadoop Streaming

    20:56
  • 1

    Hands on “Word Count” in Map/Reduce in Eclipse

    20:56
  • 2

    Sorting files using Hadoop Configuration API discussion

    20:56
  • 3

    Emulating “grep” for searching inside a file in Hadoop

    20:56
  • 4

    Chain Mapping API discussion

    20:56
  • 5

    Job Dependency API discussion and Hands on

    20:56
  • 6

    Input Format API discussion and hands on

    20:56
  • 7

    Input Split API discussion and hands on

    20:56
  • 8

    Custom Data type creation in Hadoop

    20:56
  • 10

    Hive Unstructured Data Analyzation

    20:56
  • 11

    Hive Semi structured Data Analyzation

    20:56
  • 13

    HBase Overview and Architecture

    20:56
  • 14

    HBase Installation

    20:56
  • 16

    CRUD operations

    20:56
  • 17

    Scanning and Batching

    20:56
  • 19

    HBase Key Design

    20:56
  • 21

    Zoo Keeper Overview

    20:56
  • 23

    Server Mantainace

    20:56
  • 25

    Sqoop Overview

    20:56
  • 27

    Imports and Exports

    20:56
  • 2

    Important Directories

    20:56
  • 3

    Selecting Machines

    20:56
  • 4

    Cluster Configurations

    20:56
  • 5

    Small Clusters: 2-10 Nodes

    20:56
  • 6

    Medium Clusters: 10-40 Nodes

    20:56
  • 7

    Large Clusters: Multiple Racks

    20:56
  • 1

    Configuring Hadoop API on Eclipse IDE

    20:56
  • 2

    Connecting Eclipse IDE to HDFS

    20:56
  • 1

    Distributed installations

    20:56
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