Miracle India is a Noida based one of the best Big Data Hadoop Training Institute in Delhi NCR. Join Hadoop Training and Get 100% placement support

2. The BigData
 Data! Data! BigData!
 Data Economy
 Data Analytics
 Data Science
 Traditional Data Processing Technologies
Apache Hadoop Architecture and Ecosystem
 Hadoop Background
 Hadoop Architecture
 Hadoop and RDBMS
 Hadoop Subprojects
 Hadoop Distributions
 Hadoop Documentation
 Introduction to Hadoop
 Introduction to Big Data
 Hadoop Ecosystem - Concepts
 Hadoop Map Reduce Concepts and Features
Setting up Hadoop
 Installing Hadoop
 Configuring Hadoop
 Starting Hadoop
 Running Hadoop Clients
 Browsing Hadoop UI Consoles
 How Hadoop can solve problem associated with traditional large
scale system
 Other Open Source Software related to Hadoop
 IN depth Knowledge on how Big Data Solutions work on Cloud
 How to create your own Hadoop Cluster
 Understand the main Hadoop Components
 Setup Hadoop
 Pseudo Mode
 Cluster Mode
 Ipv6
 Installation of Java , Hadoop
 Configuration of Hadoop
 Hadoop Processes
 Name Node , Secondary Name
Node
 Job Tracker , Task Tracker

3.  Data Node
 HDFS - Hadoop Distributed File System
 Learn How HDFS Works
 HDFS Design and Architecture
 HDFS Concepts
 Interacting HDFS using command Line
 Interacting HDFS using Java APIs
 Dataflow
 Blocks
 Replica
 List Data Access patterns for which HDFS is Designed
 Learn how data is stored in HDFC Cluster
 Learn HDFC Commands
 LINUX Basics , Installation and Commands
HDFS Architecture
 Hadoop 1.0 HDFS Architecture
 Hadoop 1.0 HDFS Architectural Capabilities - – Performance, Scalability,
Availability, Installability, Comnfigurability, Operability, Usability, Security
 Hadoop 2.0 HDFS Architecture
MapReduce Architecture
 Hadoop 1.0 MapReduce Architecture
 Hadoop 1.0 MapReduce Architectural Capabilities – Performance,
Scalability, Availability, Installability, Comnfigurability, Operability,
Usability, Programmability
 Hadoop 2.0 MapReduce Architecture
MapReduce Programming Basics
 MapReduce Programming Concepts – Map Phase and Reduce Phase
 MapReduce API – Key Java Classes and their Hierarchy
 Steps to Write a MapReduce Program
MapReduce Programming Intermediate
 Setting Mapper Counts and Reducer Counts
 MapReduce Configuration
 Combiners
 Partitioners
 Speculative Execution
 Task JVM Reuse
 Compression
 Understands the concepts of map and reduce operations
 Developing Map reduce Applications
 Describes how Hadoop execute a MapReduce Job
 Phases in Map reduce Framework
 Map reduce Input and Output Formats

4.  Advanced Concepts
 Sample Application
 Combiner
 Joining Datasets in Map reduce Jobs
 Map - Side Join
 Reduce - Side Join
 Map reduce customization
 Custom Input Format class
 Hash Partitioner
 Custom Partitioner
 Sorting Technique
 Custom Output Format class
 Writing a Map reduce programe
 The Map reduce Flow
 Examining a sample Map reduce programe
 Basic Map reduce API concepts
 The Mapper
 The Reducer
Querying Data using Hive
 Hive Background
 Hive Architecture
 Downloading, Installing and Configuring Hive
 Simple Hive Example
 Loading Data into Hive
 Hive Query Statements
 Hive Schema Violations
 Using Built-in Hive Functions
 Partitioning Data using Hive
 Joining Data
Querying Data using Pig
 An overview of Pig, Hive and JAQL
 Pig Background
 Architecture
 Downloading, Installing and Configuring Pig
 Running Pig
 Pig Latin Language Basics
 Core Relational Operators – DISTINCT, FILTER, SPLIT, ORDER BY, LIMIT,
GROUP, FOREACH
 Built-in Functions

5.  Relational Join Operators
 Debug Operators
 Working with Hive
 Working with JAQL
 Working with Pig
Real-time Database using HBase
 HBase Overview
 Data Model
 Architecture
 Downloading, Installing and Configuring HBase
 HBase Shell
 HBase Java API for CRUD Operations
 Hive and JAQL Transcript
 Querying Data with Pig , Hive and JAQL
 Understand how to transfer data into Hadoop using Flume
Flume
 Introduction to Flume
 Introduction to Flume Transcript
 Working with Flume
 Flume mode of operation and configuration
Sqoop
 Introduction to Sqoop
 Import Data
 Export Data
 Sqoop Syntax
 Databases connection
 Hands on Exercise
 Introduction to Flume
 Configuration and Setup
 Flume sink with example
 Channel
 Flume source with example
 Complex Flume Architecture
Advance Hadoop Concepts
 IMPALA Concepts
 HUE Concepts
 OOZIE Concepts

6. Miracle Corporate Solutions Pvt Ltd,
B-12, Sec 2 Noida – 201301
Mind Direct FZ LLC
G06, Block 2B, Dubai Knowledge Village
Dubai, UAE
Postal Code: 502973
USA
177, Addis Dr.
Churchville PA 18966.
Email :
anupamindia777@gmail.com
Website : www.miracleindia.com