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Big Data Analytics using Hadoop

Indian : Rs.9900
International : $195
INTRODUCTION
Master concepts of big data Hadoop such as HDFS (Hadoop Distributed File System), Map Reduce, Hadoop Eco System components working on live streaming data.
PROGRAM HIGHLIGHTS
  • Tool based learning
  • Learn from an industry expert
  • Live streaming data
CONTENT
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
BENIFITS
  • 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.
Frequently Asked Questions


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