Big Data Hadoop Training In Omr

❖ What is Big Data
❖ Evolution of Big Data
❖ Benefits of Big Data
❖ Operational vs Analytical Big Data
❖ Need for Big Data Analytics
❖ Big Data Challenges

❖ Master Nodes
❖ Name Node
❖ Secondary Name Node
❖ Job Tracker
❖ Client Nodes
❖ Slaves
❖ Hadoop configuration
❖ Setting up a Hadoop cluster

❖ Introduction to HDFS
❖ HDFS Features
❖ HDFS Architecture
❖ Blocks
❖ Goals of HDFS
❖ The Name node & Data Node
❖ Secondary Name node
❖ The Job Tracker
❖ The Process of a File Read
❖ How does a File Write work
❖ Data Replication
❖ Rack Awareness
❖ HDFS Federation
❖ Configuring HDFS
❖ HDFS Web Interface
❖ Fault tolerance
❖ Name node failure management
❖ Access HDFS from Java

❖ Introduction to Yarn
❖ Why Yarn
❖ Classic MapReduce v/s Yarn
❖ Advantages of Yarn
❖ Yarn Architecture
❖ Resource Manager
❖ Node Manager
❖ Application Master
❖ Application submission in YARN
❖ Node Manager containers
❖ Resource Manager components
❖ Yarn applications
❖ Scheduling in Yarn
❖ Fair Scheduler
❖ Capacity Scheduler
❖ Fault tolerance

❖ What is MapReduce
❖ Why MapReduce
❖ How MapReduce works
❖ Difference between Hadoop 1 & Hadoop 2
❖ Identity mapper & reducer
❖ Data flow in MapReduce
❖ Input Splits
❖ Relation Between Input Splits and HDFS Blocks
❖ Flow of Job Submission in MapReduce
❖ Job submission & Monitoring
❖ MapReduce algorithms
❖ Sorting
❖ Searching
❖ Indexing
❖ TF-IDF

❖ What is Hadoop
❖ History of Hadoop
❖ Hadoop Architecture
❖ Hadoop Ecosystem Components
❖ How does Hadoop work
❖ Why Hadoop & Big Data
❖ Hadoop Cluster introduction
❖ Cluster Modes
❖ Standalone
❖ Pseudo-distributed
❖ Fully – distributed
❖ HDFS Overview
❖ Introduction to MapReduce
❖ Hadoop in demand

❖ Starting HDFS
❖ Listing files in HDFS
❖ Writing a file into HDFS
❖ Reading data from HDFS
❖ Shutting down HDFS

❖ Listing contents of directory
❖ Displaying and printing disk usage
❖ Moving files & directories
❖ Copying files and directories
❖ Displaying file contents

❖ Object oriented concepts
❖ Variables and Data types
❖ Static data type
❖ Primitive data types
❖ Objects & Classes
❖ Java Operators
❖ Method and its types
❖ Constructors
❖ Conditional statements
❖ Looping in Java
❖ Access Modifiers
❖ Inheritance
❖ Polymorphism
❖ Method overloading & overriding
❖ Interfaces

❖ Hadoop data types
❖ The Mapper Class
❖ Map method
❖ The Reducer Class
❖ Shuffle Phase
❖ Sort Phase
❖ Secondary Sort
❖ Reduce Phase
❖ The Job class
❖ Job class constructor
❖ Job Context interface
❖ Combiner Class
❖ How Combiner works
❖ Record Reader
❖ Map Phase
❖ Combiner Phase
❖ Reducer Phase
❖ Record Writer
❖ Partitioners
❖ Input Data
❖ Map Tasks
❖ Partitioner Task
❖ Reduce Task
❖ Compilation & Execution

❖ What is Apache Pig?
❖ Why Apache Pig?
❖ Pig features
❖ Where should Pig be used
❖ Where not to use Pig
❖ The Pig Architecture
❖ Pig components
❖ Pig v/s MapReduce
❖ Pig v/s SQL
❖ Pig v/s Hive
❖ Pig Installation
❖ Pig Execution Modes & Mechanisms
❖ Grunt Shell Commands
❖ Pig Latin – Data Model
❖ Pig Latin Statements
❖ Pig data types
❖ Pig Latin operators
❖ Case Sensitivity
❖ Grouping & Co Grouping in Pig Latin
❖ Sorting & Filtering
❖ Joins in Pig latin
❖ Built-in Function
❖ Writing UDFs
❖ Macros in Pig

❖ What is HBase
❖ History Of HBase
❖ The NoSQL Scenario
❖ HBase & HDFS
❖ Physical Storage
❖ HBase v/s RDBMS
❖ Features of HBase
❖ HBase Data model
❖ Master server
❖ Region servers & Regions
❖ HBase Shell
❖ Create table and column family
❖ The HBase Client API

❖ Introduction to Apache Spark
❖ Features of Spark
❖ Spark built on Hadoop
❖ Components of Spark
❖ Resilient Distributed Datasets
❖ Data Sharing using Spark RDD
❖ Iterative Operations on Spark RDD
❖ Interactive Operations on Spark RDD
❖ Spark shell
❖ RDD transformations
❖ Actions
❖ Programming with RDD
❖ Start Shell
❖ Create RDD
❖ Execute Transformations
❖ Caching Transformations
❖ Applying Action
❖ Checking output
❖ GraphX overview

❖ Introducing Cloudera Impala
❖ Impala Benefits
❖ Features of Impala
❖ Relational databases vs Impala
❖ How Impala works
❖ Architecture of Impala
❖ Components of the Impala
❖ The Impala Daemon
❖ The Impala Statestore
❖ The Impala Catalog Service
❖ Query Processing Interfaces
❖ Impala Shell Command Reference
❖ Impala Data Types
❖ Creating & deleting databases and tables
❖ Inserting & overwriting table data
❖ Record Fetching and ordering
❖ Grouping records
❖ Using the Union clause
❖ Working of Impala with Hive
❖ Impala v/s Hive v/s HBase

❖ Introduction to MongoDB
❖ MongoDB v/s RDBMS
❖ Why & Where to use MongoDB
❖ Databases & Collections
❖ Inserting & querying documents
❖ Schema Design
❖ CRUD Operations

❖ Introduction to Apache Oozie
❖ Oozie Workflow
❖ Oozie Coordinators
❖ Property File
❖ Oozie Bundle system
❖ CLI and extensions
❖ Overview of Hue

❖ What is Hive?
❖ Features of Hive
❖ The Hive Architecture
❖ Components of Hive
❖ Installation & configuration
❖ Primitive types
❖ Complex types
❖ Built in functions
❖ Hive UDFs
❖ Views & Indexes
❖ Hive Data Models
❖ Hive vs Pig
❖ Co-groups
❖ Importing data
❖ Hive DDL statements
❖ Hive Query Language
❖ Data types & Operators
❖ Type conversions
❖ Joins
❖ Sorting & controlling data flow
❖ local vs mapreduce mode
❖ Partitions
❖ Buckets

❖ Introducing Sqoop
❖ Scoop installation
❖ Working of Sqoop
❖ Understanding connectors
❖ Importing data from MySQL to Hadoop HDFS
❖ Selective imports
❖ Importing data to Hive
❖ Importing to Hbase
❖ Exporting data to MySQL from Hadoop
❖ Controlling import process

❖ What is Flume?
❖ Applications of Flume
❖ Advantages of Flume
❖ Flume architecture
❖ Data flow in Flume
❖ Flume features
❖ Flume Event
❖ Flume Agent
❖ Sources
❖ Channels
❖ Sinks
❖ Log Data in Flume

❖ Zookeeper Introduction
❖ Distributed Application
❖ Benefits of Distributed Applications
❖ Why use Zookeeper
❖ Zookeeper Architecture
❖ Hierarchial Namespace
❖ Znodes
❖ Stat structure of a Znode
❖ Electing a leader