Big data Hadoop Course

Hadoop Training in Panchkula
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Big Data (Hadoop) Course Content

Module 1: Understanding on Big Data

  • What is Big Data
  • Data Facts
  • Aspects / Principle of Big Data
  • Difference b/w Big Data & Traditional BI
  • Examples: Where to use Big Data
  • Big Data Business Opportunities
  • Distributed File System computation with Facebook Example

Module 2: Understanding on LINUX

  • Understanding File System working
  • Basic commands of LINUX
  • Shell scripting
  • Use Cases – Assignments

Module 3: Understanding of JAVA

  • Introduction to OOP’s concept
  • Understanding on Data types
  • Functions
  • Methods
  • Setup of Eclipse
  • Coding examples
  • Use Cases : Assignments

Module 4: Understanding of Python

  • Concepts of Python
  • Data Types in Python
  • Exception Handling in Python
  • File Handling in Python

Module 5: FLUME

  • Introduction to Flume
  • Setup of Flume Components
  • Source
  • Sink
  • Channel
  • Agents
  • Use Cases – Assignments

Module 6: SPARK

  • Introduction to SPARK
  • Understanding of RDD, Contexts
  • Developing Application in SPARK
  • Use Cases – Assignments

Module 7: Hadoop 1.x Architecture

  • Understanding Hadoop Architect
  • Basic Understanding of Hadoop core components
  • In depth understanding of HDFS
  • Understanding HDFS services – NameNode & DataNode
  • Understanding on File System Read & Write
  • Real-time Cluster setup based on requirement

Module 8: Hadoop 2.x Architecture

  • Understanding YARN Architect
  • Architect Difference b/w Hadoop 1.x & Hadoop 2.x
  • Understanding File System Read & Write

Module 9: Cluster Installation

  • Hadoop 1.x
  • Environment Settings
  • Pseudo Mode Installation
  • Distributed Mode Installation
  • Basic configuration of Hadoop properties
  • Understanding in-built scripts
  • Running Basic Map Reduce code
  • Hadoop 2.x
  • Environment Settings
  • Distributed Mode Installation
  • Configuration of Hadoop properties
  • Running Basic Map Reduce Code
  • Hadoop File system commands

Module 10: SQOOP

  • Introduction to Sqoop
  • Setup of Sqoop
  • Sqoop Import commands
  • Sqoop Export commands
  • Formats in Sqoop
  • Use Cases – Assignments

Module 11: PIG – Data Flow Language

  • Introduction to PIG Latin
  • Setup of Pig
  • Independent Mode
  • Map Reduce Mode
  • Basic commands in Pig
  • Functions in Pig
  • Developing UDF’s in Java
  • Use Cases – Assignments

Module 12: Understanding of Map Reduce

  • Understanding of Map Reduce services – JobTracker &
    TaskTracker
  • Map Reduce Flow Chart
  • Map Reduce Phases
  • Mapper
  • Reducer
  • Splitting
  • Sorting
  • Shuffling
  • Combiner
  • Partitioning
  • Developing Map Reduce applications – JAVA Code
  • Developing Map Reduce applications – Python Code
  • Discussion on Input File Formats
  • Difference b/w Old MR API & New MR API
  • Use Cases – Assignments

Module 13: HIVE – Data Warehouse

  • Introduction to HIVE Architecture
  • Setup of Hive
  • Basic queries in HIVE
  • Advance Features of HIVE
  • Partitioning
  • Bucketing
  • Serialize & De-serialize
  • Query optimization in Hive
  • Use Cases – Assignments

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