Hadoop for Developers (4 days) Training Course

  1. Big Data Training
  2. Hadoop Training
  3. Hadoop for Developers (4 days) Training Course

Course Code

hadoopdev

Duration

28 hours (usually 4 days including breaks)

Requirements

  • comfortable with Java programming language (most programming exercises are in java)
  • comfortable in Linux environment (be able to navigate Linux command line, edit files using vi / nano)

Lab environment

Zero Install : There is no need to install hadoop software on students’ machines! A working hadoop cluster will be provided for students.

Students will need the following

  • an SSH client (Linux and Mac already have ssh clients, for Windows Putty is recommended)
  • a browser to access the cluster. We recommend Firefox browser

Overview

Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem.

 

Course Outline

Section 1: Introduction to Hadoop

  • hadoop history, concepts
  • eco system
  • distributions
  • high level architecture
  • hadoop myths
  • hadoop challenges
  • hardware / software
  • lab : first look at Hadoop

Section 2: HDFS

  • Design and architecture
  • concepts (horizontal scaling, replication, data locality, rack awareness)
  • Daemons : Namenode, Secondary namenode, Data node
  • communications / heart-beats
  • data integrity
  • read / write path
  • Namenode High Availability (HA), Federation
  • labs : Interacting with HDFS

Section 3 : Map Reduce

  • concepts and architecture
  • daemons (MRV1) : jobtracker / tasktracker
  • phases : driver, mapper, shuffle/sort, reducer
  • Map Reduce Version 1 and Version 2 (YARN)
  • Internals of Map Reduce
  • Introduction to Java Map Reduce program
  • labs : Running a sample MapReduce program

Section 4 : Pig

  • pig vs java map reduce
  • pig job flow
  • pig latin language
  • ETL with Pig
  • Transformations & Joins
  • User defined functions (UDF)
  • labs : writing Pig scripts to analyze data

Section 5: Hive

  • architecture and design
  • data types
  • SQL support in Hive
  • Creating Hive tables and querying
  • partitions
  • joins
  • text processing
  • labs : various labs on processing data with Hive

Section 6: HBase

  • concepts and architecture
  • hbase vs RDBMS vs cassandra
  • HBase Java API
  • Time series data on HBase
  • schema design
  • labs : Interacting with HBase using shell;   programming in HBase Java API ; Schema design exercise

Testimonials

★★★★★
★★★★★

Related Categories

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