BIG DATA HADOOP
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*Course completion certificate, Certification documents and materials, interview questions and job assistance included.

BIG DATA HADOOP Duration of Course:

40+ hours

BIG DATA HADOOP Topics Covered are:

Session 1 – Introduction to Big Data

  1.Importance of Data

  2.ESG Report on Analytics

  3.Big Data & It’s Hype

  4.What is Big Data?

  5.Structured vs Unstructured data

  6.Definition of Big Data

  7.Big Data Users & Scenarios

  8.Challenges of Big Data

  9.Why Distributed Processing?

Session 2 – Hadoop

  1.History Of Hadoop

  2.Hadoop Ecosystem

  3.Hadoop Animal Planet

  4.When to use & when not to use Hadoop

  5.What is Hadoop?

  6.Key Distinctions of Hadoop

  7.Hadoop Components/Architecture

  8.Understanding Storage Components

  9.Understanding Processing Components

  10.Anatomy Of a File Write

  11.Anatomy of  a File Read

Session 3 – Understanding Hadoop Cluster

  1.Handout discussion

  2.Walkthrough of CDH setup

  3.Hadoop Cluster Modes

  4.Hadoop Configuration files

  5.Understanding Hadoop Cluster configuration

  6.Data Ingestion to HDFS

Session 4 – MapReduce

  1.Meet MapReduce

  2.Word Count Algorithm – Traditional approach

  3.Traditional approach on a Distributed system

  4.Traditional approach – Drawbacks

  5.MapReduce approach

  6.Input & Output Forms of a MR program

  7.Map, Shuffle & Sort, Reduce Phases

  8.Workflow & Transformation of Data

  9.Word Count Code walkthrough

Session 5 – MapReduce

  1.Input Split & HDFS Block

  2.Relation between Split & Block

  3.MR Flow with Single Reduce Task

  4.MR flow with multiple Reducers

  5.Data locality Optimization

  6.Speculative Execution

Session 6 – Advanced MapReduce

  1.Combiner

  2.Partitioner

  3.Counters

  4.Hadoop Data Types

  5.Custom Data Types

  6.Input Format & Hierarchy

  7.Output Format & Hierarchy

  8.Side Data distribution – Distributed cache

Session 7 – Advanced MapReduce

  1.Joins

  2.Map side Join using Distributed cache

  3.Reduce side Join

  4.MR Unit – An Unit testing framework

Session 8 – Pig

  1.What is Pig?

  2.Why Pig?

  3.Pig vs Sql

  4.Execution Types or Modes

  5.Running Pig

  6.Pig Data types

  7.Pig Latin relational Operators

  8.Multi Query execution

  9.Pig Latin Diagnostic Operators

Session 9 – Pig

  1.Pig Latin Macro & UDF statements

  2.Pig Latin  Commands

  3.Pig Latin  Expressions

  4.Schemas

  5.Pig Functions

  6.Pig Latin File Loaders

  7.Pig UDF & executing a Pig UDF

Session 10 – Hive

  1.Introduction to Hive

  2.Pig Vs Hive

  3.Hive Limitations & Possibilities

  4.Hive Architecture

  5.Metastore

  6.Hive Data Organization

  7.Hive QL

  8.Sql vs Hive QL

  9.Hive Data types

  10.Data Storage

  11.Managed & External Tables

Session 11 – Hive

  1.Partitions & Buckets

  2.Storage Formats

  3.Built-in Serdes

  4.Importing Data

  5.Alter & Drop Commands

  6.Data Querying

Session 12 – Hive

  1.Using MR Scripts

  2.Hive Joins

  3.Sub Queries

  4.Views

  5.UDFs

Session 13 – HBase

  1.Introduction to NoSql & HBase

  2.Row & Column oriented storage

  3.Characteristics of a huge DB

  4.What is HBase?

  5.HBase Data-Model

  6.HBase vs RDBMS

  7.HBase architecture

  8.HBase in operation

  9.Loading Data into HBase

  10.HBase shell commands

  11.HBase operations through Java

  12.HBase operations through MR

Session 14 – ZooKeeper & Oozie

  1.Introduction to Zookeeper

  2.Distributed Coordination

  3.Zookeeper Data Model

  4.Zookeeper Service

  5.Zookeeper in HBase

  6.Introduction to Oozie

  7.Oozie workflow

Session 15 – Sqoop

  1.Introduction to Sqoop

  2.Sqoop design

  3.Sqoop Commands

  4.Sqoop Import & Export Commands

  5.Sqoop Incremental load Commands

Session 16 – Hadoop 2.0 & YARN

  1.Hadoop 1 Limitations

  2.HDFS Federation

  3.NameNode High Availability

  4.Introduction to YARN

  5.YARN Applications

  6.YARN Architecture

  7.Anatomy of an YARN application

Lessons Sample lesson

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  1. By Siddharth on August 13, 2018
    very informative and time saving, Thanks..:)