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Big Data Hadoop

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Duration

40 DAYS

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Online Fee

30,000

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Classroom Fee

10,000

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Students Enrolled

2000+

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Upcoming Batch Schedule for Online Training

Tecnosoft provides flexible timings to all our students. Here are the Online Training Schedule in our branches. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

  • 27-04-2020 Monday (Monday - Friday)Weekdays Regular 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionCourse Fees
  • 30-04-2020 Thursday (Monday - Friday)Weekdays Regular 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionCourse Fees
  • 25-04-2020Saturday (Saturday - Sunday)Weekend Regular11:00 AM (IST) (Class 3Hrs) / Per SessionCourse Fees
  • 25-04-2020Saturday (Saturday - Sunday)Weekend Fast-track 10:00 AM (IST)(Class 6Hrs - 7Hrs) / Per SessionCourse Fees
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Course Curriculam

Hadoop Introduction

  • What is Hadoop? Why Hadoop
  • Hadoop History
  • Different Types of Components In Hadoop
    1. HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on
  • What is the scope of Hadoop?

Hadoop Distributed File System (HDFS)(For storing a data)

  • Introduction of HDFS
  • HDFS Roles in Hadoop
  • Features of Hadoop
  • Daemons of Hadoop and its functionality
    1. Name Node
    2. Secondary Name Node
    3. Job Tracker
    4. Data Node
    5. Task Tracker
  • Nodes
  • Tracks
  • Data Center
  • Basic Configuration for HDFS
  • Data Organization, Blocks and Replication
  • Rack Awareness, Heartbeat Signal
  • How to Store the Data into HDFS
  • Accessing HDFS (Introduction of Basic UNIX commands)
  • CLI commands

Map Reduce Using Java(Processing the Data)

  • Introduction of MapReduce
  • MapReduce Architecture
  • Data flow in MapReduce
    1. Splits
    2. Mapper
    3. Portioning
    4. Sort and Shuffle
    5. Combiner
    6. Reducer
  • Basic combination of mapReduce
  • MapReduce Life Cycle
    1. Drover Code
    2. Mapper and Reducer
  • Life Cycle of Mapper
  • Life Cycle of Reducer
  • Identity Mapper
  • Identity Reducer
  • Writing and Executing the Basic MapReduce Program using Java
  • File Input/output Formats in MapReduce Jobs
    1. Text Input/output Format
    2. Key Value Input/output Format
    3. Sequence File Input/output Format
  • Partitions
    1. Hash Partitions
    2. Custom Partitions with example
  • Joins
    1. Map-side Joins
    2. Reducer-side Joins
  • Distributed Cache with example
  • Counters with example
  • Schedulers
    1. Capacity Scheduler
    2. Fair Scheduler

Pig

  • Introduction to Apache PIG
  • MapReduce vs PIG
  • Data Types in PIG
    1. Scalar Data Types with examples
    2. Complex Data Types with examples
  • Basic PIG programming
  • Modes of Execution in PIG
    1. Local Mode
    2. MapReduce Mode
  • Execution Mechanisms
    1. Grunt Shell
    2. Script
    3. Embedded
  • Operators/Transformations in PIG
  • Comparison examples with MR
    1. Word Count example
    2. Join example
  • PIG UDF’s
    1. Filter with example
    2. Eval with example

Sqoop

  • Introduction to SQOOP
  • Connect to mySql database
  • SQOOP commands
    1. Import
    2. Export
    3. Eval
    4. Codegen and etc
  • Joins in SQOOP
  • Import from MySQL to HBASE
  • Export from HDFS to MySQL

Hive

  • Introduction to HIVE
  • HIVE Meta Store
  • HIVE Architecture
  • Tables in HIVE
    1. Managed Tables with examples
    2. External Tables with examples
  • Hive Data Types
    1. Primitive Types with examples
    2. Complex Types with examples

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