Hadoop Training Material

Introduction to BigData, Hadoop:-

 Big Data Introduction
 Hadoop Introduction
 What is Hadoop? Why Hadoop?
 Hadoop History?
 Different types of Components in Hadoop?
 HDFS, MapReduce, PIG, Hive, SQOOP, HBASE, OOZIE, Flume, Zookeeper and so on…
 What is the scope of Hadoop?

Deep Drive in HDFS (for Storing the Data):-
 Introduction of HDFS
 HDFS Design
 HDFS role in Hadoop
 Features of HDFS
 Daemons of Hadoop and its functionality
o Name Node
o Secondary Name Node
o Job Tracker
o Data Node
o Task Tracker
 Anatomy of File Wright
 Anatomy of File Read
 Network Topology
o Nodes
o Racks
o Data Center
 Parallel Copying using DistCp
 Basic Configuration for HDFS
 Data Organization
o Blocks and
o Replication
 Rack Awareness
 Heartbeat Signal
 How to Store the Data into HDFS
 How to Read the Data from HDFS
 Accessing HDFS (Introduction of Basic UNIX commands)
 CLI commands

MapReduce using Java (Processing the Data):-

 The introduction of MapReduce.
 MapReduce Architecture
 Data flow in MapReduce
o Splits
o Mapper
o Portioning
o Sort and shuffle
o Combiner
o Reducer
 Understand Difference Between Block and InputSplit
 Role of RecordReader
 Basic Configuration of MapReduce
 MapReduce life cycle
o Driver Code
o Mapper
o and Reducer
 How MapReduce Works
 Writing and Executing the Basic MapReduce Program using Java
 Submission & Initialization of MapReduce Job.
 File Input/Output Formats in MapReduce Jobs
o Text Input Format
o Key Value Input Format
o Sequence File Input Format
o NLine Input Format
 Joins
o Map-side Joins
o Reducer-side Joins
 Word Count Example
 Partition MapReduce Program
 Side Data Distribution
o Distributed Cache (with Program)
 Counters (with Program)
o Types of Counters
o Task Counters
o Job Counters
o User Defined Counters
o Propagation of Counters
 Job Scheduling

PIG:-

 Introduction to Apache PIG
 Introduction to PIG Data Flow Engine
 MapReduce vs. PIG in detail
 When should PIG use?
 Data Types in PIG
 Basic PIG programming
 Modes of Execution in PIG
o Local Mode and
o MapReduce Mode
 Execution Mechanisms
o Grunt Shell
o Script
o Embedded
 Operators/Transformations in PIG
 PIG UDF’s with Program
 Word Count Example in PIG
 The difference between the MapReduce and PIG

SQOOP:-

 Introduction to SQOOP
 Use of SQOOP
 Connect to mySql database
 SQOOP commands
o Import
o Export
o Eval
o Codegen etc…
 Joins in SQOOP
 Export to MySQL
 Export to HBase

HIVE:-

 Introduction to HIVE
 HIVE Meta Store
 HIVE Architecture
 Tables in HIVE
o Managed Tables
o External Tables
 Hive Data Types
o Primitive Types
o Complex Types
 Partition
 Joins in HIVE
 HIVE UDF’s and UADF’s with Programs
 Word Count Example

HBASE:-

 Introduction to HBASE
 Basic Configurations of HBASE
 Fundamentals of HBase
 What is NoSQL?
 HBase Data Model
o Table and Row
o Column Family and Column Qualifier
o Cell and its Versioning
 Categories of NoSQL Data Bases
o Key-Value Database
o Document Database
o Column Family Database
 HBASE Architecture
o HMaster
o Region Servers
o Regions
o MemStore
o Store
 SQL vs. NOSQL
 How HBASE is differed from RDBMS
 HDFS vs. HBase
 Client-side buffering or bulk uploads
 HBase Designing Tables
 HBase Operations
o Get
o Scan
o Put
o Delete

MongoDB:--

 What is MongoDB?
 Where to Use?
 Configuration On Windows
 Inserting the data into MongoDB?
 Reading the MongoDB data.

Cluster Setup:--

 Downloading and installing the Ubuntu12.x
 Installing Java
 Installing Hadoop
 Creating Cluster
 Increasing Decreasing the Cluster size
 Monitoring the Cluster Health
 Starting and Stopping the Nodes

Zookeeper

 Introduction Zookeeper
 Data Modal
 Operations

OOZIE

 Introduction to OOZIE
 Use of OOZIE
 Where to use?

Flume

 Introduction to Flume
 Uses of Flume
 Flume Architecture
o Flume Master
o Flume Collectors

 

o Flume Agents

Project Explanation with Architecture

                                   

                                DOWNLOAD