Description

Type

Course
Level

Intermediate
Methodology

Online

Duration

Flexible
Start date

Different dates available

Master concepts of big data Hadoop such as HDFS (Hadoop Distributed File System), Map Reduce, Hadoop Eco System components working on live streaming data.

Facilities

Online

Start date

Different dates availableEnrolment now open

About this course

Course objectives

Tool based learning
Learn from an industry expert
Live streaming data

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Subjects

Apache
Installation
Workflow
Distributed systems
Ecosystem
MapReduce Paradigm
Environment Setup
Pig Installation
Flume Installation
Hue Installation
Permission Management

Teachers and trainers (1)

Name Name

Teacher

Course programme

Introduction to Big Data and Hadoop

Introduction
Challenges of Processing Big Data
Distributed Systems
History of Hadoop
Hadoop Overview
Ecosystem of Hadoop
HDFS and MapReduce Paradigm
Processing Pipeline
Big Data Technologies
Use Cases
Features of Hadoop
Summary

Hadoop Environment Setup

Introduction
Hadoop Installation and Configuration
Hive Installation and Configuration
Pig Installation and Configuration
Sqoop Installation and Configuration
Oozie Installation and Configuration
Flume Installation and Configuration
Hbase Installation and Configuration
Hue Installation and Configuration

HDFS Architecture

Introduction
HDFS Configuration Files
Data Storage in HDFS
Blocks and Splits
Metadata Files
Name Node – Demo
HDFS Data Storage – Demo
Reliability and Rack Awareness
High Availability
Data Replication – Demo
Reliable Storage – Demo
HDFS Client
Data Node – Demo
HDFS Clients – Demo
Summary

HDFS Commands

Introduction
HDFS Commands
Basic HDFS Commands - Demo
Read Anatomy in HDFS
Write Anatomy in HDFS
Additional HDFS Commands - Demo
HDFS File System API
HDFS File System API - Demo
HDFS Permission Management
Permission Management – Demo
Summary

MapReduce

MapReduce 1 Architecture
MR and Traditional Approach
Architecture 1
Introduction to YARN
Architecture 2
Summary

MapReduce Programs

Introduction
Executing a MapReduce Program - Demo
Datatypes and APIs
MapReduce Concepts
Mapper – Demo
MapReduce – Demo
Combiners – Demo
Partitioners – Demo
Debug logs & Printing in MR Jobs – Demo
Path Filters – Demo
Splits – Demo
Named Output – Demo Summary
Write MapReduce Keys and Values – Demo
Identity Mappers – Demo
Identity Reducers – Demo
Counters in Hadoop
MapReduce Counters – Demo
Input and Output Formats
About MR Unit
MR Unit – Demo
Summary

MapReduce and Job Execution

Introduction
Job Flow
Job Submission
Job Initializing
Job Scheduling
Map Task Execution
Sort and Shuffle
Reduce Task Execution
Job Cleanup
Job Failure – Demo
Staggering Job – Demo
Scheduler
Summary

Hadoop Serialization and Compression

Introduction - Serialization
Uses of Serialization
Serialization Techniques
Summary
Introduction - Compression
Uses of Compression
Compression Techniques
Summary

Advanced MapReduce Programming

Introduction
Customization of Input Format APIs
Input Format and Record Readers – Demo
Distributed Cache
Distributed Cache – Demo
Map Side Joins
Sideways Joins
Map Side Joins – Demo
Reduce Side Joins
Reduce Side Joins – Demo
Sequence File Format
Sequence File Creation – Demo
Sequence File with MapReduce -Demo
Hadoop Streaming
Hadoop Streaming – Demo
Configuring Development Environment using Eclipse – Demo
Running MapReduce Jobs – Demo
Summary

Apache Hive

Introduction
Hive vs RDBMS
Hive Architecture
Hive Components
Hive Schema Model
Hive Integration with Hadoop
Hive Query Language
Transformations in Hive
Hive Database Creation – Demo
Hive Tables – Demo
Hive Queries - Demo
Advanced Hive Partitioning – Demo
Bucketing – Demo
Advanced Concepts – Demo
Manage an XML or JSON files – Demo
Use a predefined Serde – Demo
Summary

Apache Pig

Introduction
MapReduce and Pig
Modes of Execution in Pig
Pig Client
Pig Datatypes and Operators
SQL vs Apache Pig
Pig Usage
Loading Data in Pig – Demo
Pig Dialects – Demo
Transformations in Pig – Demo
Debugging in Pig – Demo
Other capabilities in Pig - Demo

Apache HBase

Introduction
Categories of NoSQL Databases
Hbase Evolution
Hbase vs RDBMS
Hbase Architecture
Hbase Components
Column Family
Hbase Fundamentals
Hbase Storage
Hbase Client
Basic CRUD Operation
Basic CRUD Operation – Demo
Zookeeper
Zookeeper – Demo
Summary

Apache Sqoop, Oozie and Hue

Introduction - Apache Sqoop
Sqoop Usage
Working with Sqoop – Demo
Advanced Sqoop
Hive Integration – Demo
Hbase Integration – Demo
Sqoop Scripts - Demo
Introduction - Apache Oozie
Oozie Client
Basic Workflow Setup
Types of Oozie Actions
Control Statements
Defining a Workflow
Run MapReduce with Oozie - Demo
Summary

Introduction - Apache Hue

Hue User Interface
Working with Hive using Hue
Working with Pig using Hue
Monitoring an Oozie Job using Hue
Apache Hue – Demo
Summary

Streaming

Introduction Summary
Apache Flume Introduction
Flume Core Components
Launch Flume
Apache Flume – Demo
Apache Spark and Storm Introduction
Storm Concepts
Spark Streaming Concepts
Deployment Architecture
Summary

Hadoop Real Time Deployment and Distribution

Introduction - Real Time Deployment
System Architecture
Logical Deployment Overview
Physical Deployment Overview
Summary
Introduction - Big Data Software and Tools
Streaming Tools
NOSQL Tools
Workflow Tools
Administration Tools
Other Ecosystem Tools
Summary

Additional information

BENEFITS

Learn to derive business insights from large and complex data. Gain in depth knowledge of Big Data Analytics concepts and tools. Develop analytical and decision making skills by attempting real life projects. Get an Industry recognised certificate in Big Data Analytics from Manipal ProLearn. Find opportunities as Data Scientists, Big Data Engineers, Business Analytics Specialist etc. Big Data Specialists earn salaries anywhere between 6-15lac per annum.

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Big Data Analytics using Hadoop

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Big Data Analytics using Hadoop

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and compare them to help you choose.