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Big Data and Hadoop

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Big Data and Hadoop Training Content

Hadoop Training

1. Introduction

  • What is Data
  • Types of Data
  • What is BigData
  • Sources of BigData
  • Traditional way to Store Bigdata
  • Characteristics of BigData
  • How Bigdata becomes Problem
  • That is Hadoop
  • History of Hadoop

2. Archetecture

  • Archetecture of Hadoop
  • Data Storage and Analysis
  • Comparison with Other systems
  • RDBMS v/s Hadoop
  • Ecosystem
  • Hdfs
  • The design of hdfs
  • Hdfs concepts
  • Blocks
  • Namenodes and Datanodes
  • HDFS Federation
  • HDFS High-Availability
  • Difference B/w Hadoop 1.x.x and 2.x.x
  • Introduction of Cluster
  • Hadoop Daemom

3. Vm Ware

  • Virtualization
  • Intro of vm ware
  • Vm ware Arch.
  • Types Of vm ware
  • Software Distribution
  • Horton Imports
  • Linux Command
  • Hadoop commands
  • Run a map reduce app.

4. Installation

  • Ubuntu installation
  • Installation of Hadoop on real machine.
  • Basic commands

5. Apache MapReduce

  • JobTracker and TaskTracker
  • Topology Hadoop cluster
  • Example of MapReduce
  • Map Function
  • Reduce Function
  • Java Implementation of MapReduce
  • DataFlow of MapReduce
  • Anatomy of MapReduce Job (MR-1)
  • Submission & Initialization of MapReduce Job (What Happen ?)
  • Assigning & Execution of Tasks
  • Monitoring & Progress of MapReduce Job
  • Completion of Job
  • Handling of MapReduce Job
  • Limitation of Current Architecture (Classic)
  • What are the Requirement ?
  • Introduction of YARN
  • YARN Architecture

6. Apache Hive

  • installing hive
  • the hive shell
  • an ex.
  • Running hive
  • configuring hive
  • hive services
  • comparison with traditional databases
  • schema on read versus schema on write
  • updates, transactions,and indexes
  • hiveQl
  • Data types
  • Operators and func.
  • tables
  • managed tables and external table
  • partitions and buckets
  • storage formats
  • importing data
  • altering tables
  • dropping tables
  • querying data
  • sorting and aggregating
  • map reduce scripts
  • joins
  • subqueries
  • Partitioning
  • Bucketing

7. Apache Pig

  • Installing and running Pig
  • Execution Types
  • Running Pig Programs
  • Grunt
  • pig latin editors
  • an ex.
  • generating ex.
  • comparison with databases
  • pig latin
  • structure
  • statements
  • expressions
  • types
  • schemas
  • functions
  • macros
  • user-defined func.
  • a filter udf
  • an eval udf
  • a load udf
  • data processing operators
  • loading and storing data
  • filtering data
  • grouping and joining data
  • sorting data
  • combining and splitting data

 8. Apache Sqoop

  • Introduction of Sqoop
  • Installation of Sqoop
  • How Sqoop Works
  • Configuring Bashrc
  • Download and configure Mysql
  • Installation of Mysql
  • Import table
  • Importing into Target Directory
  • Import Subset of table data
  • Alter a table
  • Export a table

9. Apache Hbase

  • Installing and running Hbase
  • Basic Commands
  • How to connect Hive With Hbase
  • How to update row
  • How to delete row
  • How to alter table

10. Apache Flume

  • Introduction of Flume
  • Installing flume
  • Architecture
  • Streaming of data
  • Insulate System
  • Data Delivery
  • Scale Horizontally
  • How Flume Works
  • Its Components:-
  • Events
  • Source
  • Sinks
  • Channel
  • Agent
  • Client
  • Live Data Fetching
  • Twitts Data Fetch

 11. Apache Zookeeper

  • Introduction to Zookeeper
  • Zookeeper Installation
  • Basic Commands

 12. Cluster

  • Install multinode cluster
  • Configuration on Multinode

 13. MongoDB

  • Introduction
  • Installation
  • Basic Commands

14. Apache Spark

  • Introduction of Spark
  • Installation of Spark
  • Introduction of Scala
  • Installation of Scala

15. Hadoop installation on Cloud

  • Configure EC2
  • How to make instance
  • Configure instance
  • Excess instance through putty
  • Installation of Hadoop on Cloud

16. Cassandra

  • Introduction
  • How to install Cassandra
  • Basic Commands

 17. Projects

  • Sentiments data analysis using Hadoop
  • Demonetization analysis through pig
  • Working of Hadoop in real word using Sqoop Jobs
  • Data Compression using Hive
  • Comparative analysis Between Hive and Pig.

Training Modes

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