Hadoop Overview

Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

What is Big Data?

Big data means really a big data, it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data, rather it has become a complete subject, which involves various tools, techniques and frameworks.

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Hadoop Course Content

Big-Data and Hadoop

• • Introduction to big data and Hadoop
  • Hadoop Architecture
  • Installing Ubuntu with Java 1.8 on VM Workstation 11
  • Hadoop Versioning and Configuration
  • Single Node Hadoop 1.2.1 installation on Ubuntu 14.4.1
  • Multi Node Hadoop 1.2.1 installation on Ubuntu 14.4.1
  • Linux commands and Hadoop commands
  • Cluster architecture and block placement
  • Modes in Hadoop
  • Local Mode
  • Pseudo Distributed Mode
  • Fully Distributed Mode
  • Hadoop Daemon
  • Master Daemons(Name Node, Secondary Name Node, Job Tracker)
  • Slave Daemons(Job tracker, Task tracker)
  • Task Instance
  • Hadoop HDFS Commands
  • Accessing HDFS
  • CLI Approach
  • Java Approach

Map-Reduce

• • Understanding Map Reduce Framework
  • Inspiration to Word-Count Example
  • Developing Map-Reduce Program using Eclipse Luna
  • HDFS Read-Write Process
  • Map-Reduce Life Cycle Method
  • Serialization(Java)
  • Datatypes
  • Comparator and Comparable(Java)
  • Custom Output File
  • Analysing Temperature dataset using Map-Reduce
  • Custom Partitioner & Combiner
  • Running Map-Reduce in Local and Pseudo Distributed Mode.

Advanced Map-Reduce

• • Enum(Java)
  • Custom and Dynamic Counters
  • Running Map-Reduce in Multi-node Hadoop Cluster
  • Custom Writable
  • Site Data Distribution
  • Using Configuration
  • Using DistributedCache
  • Using stringifie
  • Input Formatters
  • NLine Input Formatter
  • XML Input Formatter
  • Sorting
  • Reverse Sorting
  • Secondary Sorting
  • Compression Technique
  • Working with Sequence File Format
  • Working with AVRO File Format
  • Testing MapReduce with MR Unit
  • Working with NYSE DataSets
  • Working with Million Song DataSets
  • Running Map-Reduce in Cloudera Box

HIVE

• • Hive Introduction & Installation
  • Data Types in Hive
  • Commands in Hive
  • ExploringInternal and External Table
  • Partitions
  • Complex data types
  • UDF in Hive
  • Built-in UDF
  • Custom UDF
  • Thrift Server
  • Java to Hive Connection
  • Joins in Hive
  • Working with HWI
  • Bucket Map-side Join
  • More commands
  • View
  • SortBy
  • Distribute By
  • Lateral View
  • Running Hive in Cloudera

SQOOP

• • Sqoop Installations and Basics
  • Importing Data from Oracle to HDFS
  • Advance Imports
  • Real Time UseCase
  • Exporting Data from HDFS to Oracle
  • Running Sqoop in Cloudera

PIG

• • Installation and Introduction
  • WordCount in Pig
  • NYSE in Pig
  • Working With Complex Datatypes
  • Pig Schema
  • Miscellaneous Command
  • Group
  • Filter
  • Order
  • Distinct
  • Join
  • Flatten
  • Co-group
  • Union
  • Illustrate
  • Explain
  • UDFs in Pig
  • Parameter Substitution and DryRun
  • Pig Macros
  • Running Pig in Cloudera

OOZIE

• Installing Oozie
• Running Map-Reduce with Oozie
• Running Pig and Sqoop with Oozie