SKU: WOO-ALBUM-3 Categories: ,

Course Description

Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation

Course Content


Chapter-1 Understanding Big Data and Hadoop


  • What Is Big Data.
  • Limitations and Solutions of existing Data Analytics Architecture
  • Hadoop Features
  • Hadoop Ecosystem
  • Hadoop 2.x core components
  • Hadoop Storage : HDFS
  • Hadoop Processing: MapReduce Framework


Chapter-2 Hadoop Architecture and HDFS

  • Hadoop 2.x Cluster Architecture – Federation and High Availability
  • A Typical Production Hadoop Cluster
  • Hadoop Cluster Modes
  • Common Hadoop Shell Commands
  • Hadoop 2.x Configuration File
  • Single node cluster and Multi node cluster set up Hadoop Administration


Chapter-3 Hadoop MapReduce Framework

  • MapReduce Use Cases
  • Traditional way Vs MapReduce way
  • Why MapReduce
  • Hadoop 2.x MapReduce Architecture
  • Hadoop 2.x MapReduce Components
  • YARN MR Application Execution Flow
  • YARN Workflow
  • Demo on MapReduce
  • Input Splits
  • Relation between Input Splits and HDFS Blocks
  • MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set
  • Demo on Weather Data set.


.Chapter-4    Advanced MapReduce


  • Counters
  • Distributed Cache
  • MRunit,
  • Reduce Join
  • Custom Input Format
  • Sequence Input Format
  • Xml file Parsing using MapReduce.


Chapter-5    Pig


  • About Pig
  • MapReduce Vs Pig
  • Pig Use Cases , Programming Structure in Pig, Pig Running Modes, Pig components, Pig Execution, Pig Latin Program, Data Models in Pig, Pig Data Types, Shell and Utility Command


  • Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Specialized joins in Pig, Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function,


  • Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution ), Pig Streaming, Testing Pig scripts with Punit, Aviation use case in PIG, Pig Demo on Healthcare Data set.

Chapter-6    Hive


  • Hive Background, Hive Use Case, About Hive, Hive Vs Pig, Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database, Hive Data Types and Data Models, Partitions and Buckets, Hive Tables(Managed Tables and External Tables), Importing Data, Querying Data, Managing Outputs, Hive Script, Hive UDF, Retail use case in Hive, Hive Demo on Healthcare Data set.


Chapter- 7 Advanced Hive and Hbase


  • Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive Indexes and views Hive query optimizers, Hive : Thrift Server, User Defined Functions, HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, Run Modes & Configuration, HBase Cluster Deployment.


Chapter – 8      Advanced Hbase


  • HBase Data Model, HBase Shell, HBase Client API, Data Loading Techniques, ZooKeeper Data Model, Zookeeper Service, Zookeeper, Demos on Bulk Loading, Getting and Inserting Data, Filters in HBase.






Chapter- 9  Processing Distributed Data with Apache Spark


  • What is Apache Spark, Spark Ecosystem, Spark Components, History of Spark and Spark Versions/Releases, Spark a Polyglot, What is Scala?, Why Scala?, SparkContext, RDD.


Chapter- 10  Oozie and Hadoop Project


  • Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web Console, Oozie for MapReduce, PIG, Hive, and Sqoop, Combine flow of MR, PIG, Hive in Oozie, Hadoop Project Demo, Hadoop Integration with Talend.