Hadoop and Big-data Training



At the Completion on said course the students will have full professional knowlege of coding skills, developing logics & websites/applications, working on live projects.We are the best providers of Hadoop with big-data Training in navi mumbai, kharghar with excellent placements. By giving the perfect Hadoop with big-data Training in navi mumbai, we differ very much from others.By the expert guidance in learning Hadoop with big-data Training in navi mumbai we can proudly say we are the top providers.


Target Audience

Graduates Post-Graduates & working professional, Job aspirants

Pre-requisites

C & OOPS Concepts would be an advantage

Duration

45 days Duration, Classes taken 5 Days a week


Batches

5 day in a week, Weekend batch, Only Sunday batch

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Fee Structure

Starting from 15000/-


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    MODULE 1 : Hadoop and its Architecture

  1. What is Big Data
  2. Hadoop Architecture
  3. Hadoop ecosystem components
  4. Hadoop Storage: HDFS
  5. Hadoop Processing: MapReduce Framework
  6. Anatomy of File Write and Read
  7. Hadoop Check-Pointing
  8. Rack Awareness

    MODULE 2 : Pseudo-Cluster Configuration

  1. Hadoop Cluster Architecture
  2. Hadoop Cluster Configuration files
  3. Hadoop Cluster Modes
  4. Multi-Node Hadoop Cluster
  5. A Typical Production Hadoop Cluster
  6. MapReduce Job execution
  7. Common Hadoop Shell commands

    MODULE 3 : Hadoop MapReduce framework

  1. Hadoop Data Types
  2. Hadoop MapReduce paradigm
  3. Map and Reduce tasks
  4. Map Reduce Execution Framework
  5. Hands-on Map-Reduce Programming

    MODULE 4 : Pig Latin

  1. Installing and Running Pig
  2. Grunt
  3. Pig's Data Model
  4. Pig Latin
  5. Developing and Testing Pig Latin Scripts
  6. Filter, Load & Store, Join and many more Functions
  7. Hadoop Project: Pig Scripting.

    MODULE 5 : Hive (HQL - Hive Query Language)

  1. Hive Architecture and Installation
  2. Comparison with Traditional Database
  3. HiveQL: Data Types
  4. Operators and Functions
  5. Hive Tables(Managed Tables and External Tables, Partitions and Buckets, Importing Data, Altering Tables, Dropping Tables)

    MODULE 6 : Sqoop & Flume

  1. Sqoop Architecture
  2. Working with Sqoop commands
  3. Importing single/all tables from RDBMS
  4. Exporting data to RDBMS
  5. Importing RDBMS data into Hive table
  6. Flume architecture
  7. How to write a Flume configuration file
  8. Importing real time data using Flume

    MODULE 7 : Hadoop's NOSql Database: HBase

  1. Introduction to HBase
  2. CAP Theorem
  3. HBase Architecture
  4. Minor and Major Compaction
  5. Creating HBase tables
  6. Querying HBase tables

    MODULE 1: Hadoop and its Architecture

  1. What is Big Data
  2. Hadoop Architecture
  3. Hadoop ecosystem components
  4. Hadoop Storage: HDFS
  5. Hadoop Processing: MapReduce Framework
  6. Anatomy of File Write and Read
  7. Hadoop Check-Pointing
  8. Rack Awareness

    MODULE 2 : Pseudo-Cluster Configuration

  1. Hadoop Cluster Architecture
  2. Hadoop Cluster Configuration files
  3. Hadoop Cluster Modes
  4. Multi-Node Hadoop Cluster
  5. A Typical Production Hadoop Cluster
  6. MapReduce Job execution
  7. Common Hadoop Shell commands

    MODULE 3 : Hadoop MapReduce framework

  1. Hadoop Data Types
  2. Hadoop MapReduce paradigm
  3. Map and Reduce tasks
  4. Map Reduce Execution Framework
  5. Hands-on Map-Reduce Programming
  6. Partitioners and Combiners

    MODULE 4 : Pig Latin

  1. Installing and Running Pig
  2. Grunt
  3. Pig's Data Model
  4. Pig Latin
  5. Developing and Testing Pig Latin Scripts
  6. Filter, Load & Store, Join and many more Functions
  7. Hadoop Project: Pig Scripting.
  8. Advanced Pig functions
  9. XML parsing with Pig
  10. User-Defined Functions (UDF)
  11. Date Functions
  12. HCatalog

    MODULE 5 : Hive (HQL - Hive Query Language)

  1. Hive Architecture and Installation
  2. Comparison with Traditional Database
  3. HiveQL: Data Types
  4. Operators and Functions
  5. Hive Tables(Managed Tables and External Tables, Partitions and Buckets, Importing Data, Altering Tables, Dropping Tables)

    MODULE 6 : Advance Hive CTAS

  1. Appending Data into existing Hive Table
  2. Querying Data (Sorting and Aggregating, Joins & Sub-queries, Views, Map side Joins to optimize Query)

    MODULE 7 : Sqoop & Flume

  1. Sqoop Architecture
  2. Working with Sqoop commands
  3. Importing single/all tables from RDBMS
  4. Incremental imports
  5. Exporting data to RDBMS
  6. Importing RDBMS data into Hive table
  7. Flume architecture
  8. How to write a Flume configuration file
  9. Importing real time data using Flume

    MODULE 8 : Hadoop's NOSql Database: HBase

  1. Introduction to HBase
  2. CAP Theorem
  3. HBase Architecture
  4. Minor and Major Compaction
  5. Creating HBase tables
  6. Querying HBase tables

    MODULE 9 : Introduction to Oozie and HUE

  1. Creating jobs with Oozie.
  2. Scheduling Jobs with Oozie
  3. Creating workflows for automated processing
  4. Introduction to HUE
  5. Job Monitoring through HUE
  6. Writing Pig / Hive scripts in HUE
  7. Metastore tables / HBase tables in HUE