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Hadoop Online Training & Free Hadoop Online Training Courses

About Hadoop Online Training

Best Online Training Institute is a leading online training provider with real-time and placement oriented Hadoop Courses. Our Team of Hadoop Trainers are certified experts and working Professionals with hands on experience on real time Hadoop Projects. 

Our Hadoop Online Training course is custom made and is suitable for both working professionals and beginners which includes basic to advanced levels. we provide easy to learn free video tutorials, course content, interview questions and training material for our Hadoop Online Training course along with 24/7 online support from our expert trainers.

Contact us today for Free Online Video Classes on Hadoop Online Training Course.


Hadoop Online Training Course Content

Hadoop training course content and Syllabus

Hadoop Course Content

Hadoop Overview, Architecture Considerations, Infrastructure, Platforms and Automation
Use case walkthrough
ETL
Log Analytics
Real Time Analytics
Hbase for Developers :

  • NoSQL Introduction
  • Traditional RDBMS approach
  • NoSQL introduction
  • Hadoop & Hbase positioning
  • Hbase Introduction
  • What it is, what it is not, its history and common use-cases
  • Hbase Client – Shell, exercise
  • Hbase Architecture
  • Building Components
  • Storage, B+ tree, Log Structured Merge Trees
  • Region Lifecycle
  • Read/Write Path
  • Hbase Schema Design
  • Introduction to hbase schema
  • Column Family, Rows, Cells, Cell timestamp
  • Deletes
  • Exercise - build a schema, load data, query data
  • Hbase Java API – Exercises
  • Connection
  • CRUD API
  • Scan API
  • Filters
  • Counters
  • Hbase MapReduce
  • Hbase Bulk load
  • Hbase Operations, cluster management
  • Performance Tuning
  • Advanced Features
  • Exercise
  • Recap and Q&A
  • MapReduce for Developers

Introduction
  • Traditional Systems / Why Big Data / Why Hadoop
  • Hadoop Basic Concepts/Fundamentals
  • Hadoop in the Enterprise
  • Where Hadoop Fits in the Enterprise
  • Review Use Cases
  • Architecture
  • Hadoop Architecture & Building Blocks
  • HDFS and MapReduce
  • Hadoop CLI
  • Walkthrough
  • Exercise
  • MapReduce Programming
  • Fundamentals
  • Anatomy of MapReduce Job Run
  • Job Monitoring, Scheduling
  • Sample Code Walk Through
  • Hadoop API Walk Through
  • Exercise
  • MapReduce Formats
  • Input Formats, Exercise
  • Output Formats, Exercise
  • Hadoop File Formats

MapReduce Design Considerations

  • MapReduce Algorithms
  • Walkthrough of 2-3 Algorithms
  • MapReduce Features
  • Counters, Exercise
  • Map Side Join, Exercise
  • Reduce Side Join, Exercise
  • Sorting, Exercise
  • Use Case A (Long Exercise)
  • Input Formats, Exercise
  • Output Formats, Exercise
  • MapReduce Testing

  • Hadoop Ecosystem
  • Oozie
  • Flume
  • Sqoop
  • Exercise 1 (Sqoop)
  • Streaming API
  • Exercise 2 (Streaming API)
  • Hcatalog
  • Zookeeper
  • HBase Introduction
  • Introduction
  • HBase Architecture
  • MapReduce Performance Tuning

Development Best Practice and Debugging

Apache Hadoop for Administrators

  • Hadoop Fundamentals and Architecture
  • Why Hadoop, Hadoop Basics and Hadoop Architecture
  • HDFS and Map Reduce
  • Hadoop Ecosystems Overview
  • Hive
  • Hbase
  • ZooKeeper
  • Pig
  • Mahout
  • Flume
  • Sqoop
  • Oozie
  • Hardware and Software requirements
  • Hardware, Operating System and Other Software
  • Management Console
  • Deploy Hadoop ecosystem services
  • Hive
  • ZooKeeper
  • HBase
  • Administration
  • Pig
  • Mahout
  • Mysql
  • Setup Security
  • Enable Security – Configure Users, Groups, Secure HDFS, MapReduce, HBase and Hive
  • Configuring User and Groups
  • Configuring Secure HDFS
  • Configuring Secure MapReduce
  • Configuring Secure HBase and Hive
  • Manage and Monitor your cluster

Command Line Interface

Troubleshooting your cluster

Introduction to Big Data and Hadoop

  • Hadoop Overview
  • Why Hadoop
  • Hadoop Basic Concepts
  • Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout
  • Where Hadoop fits in the Enterprise
  • Review use cases
  • Apache Hive & Pig for Developers

  • Overview of Hadoop
  • Big Data and the Distributed File System
  • MapReduce
  • Hive Introduction
  • Why Hive?
  • Compare vs SQL
  • Use Cases
  • Hive Architecture – Building Blocks
  • Hive CLI and Language (Exercise)
  • HDFS Shell
  • Hive CLI
  • Data Types
  • Hive Cheat-Sheet
  • Data Definition Statements
  • Data Manipulation Statements
  • Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
  • Built-in Functions
  • Union, Sub Queries, Sampling, Explain
  • Hive Usecase implementation - (Exercise)
  • Use Case 1
  • Use Case 2
  • Best Practices
  • Advance Features
  • Transform and Map-Reduce Scripts
  • Custom UDF
  • UDTF
  • SerDe
  • Recap and Q&A
  • Pig Introduction
  • Position Pig in Hadoop ecosystem
  • Why Pig and not MapReduce
  • Simple example (slides) comparing Pig and MapReduce
  • Who is using Pig now and what are the main use cases
  • Pig Architecture
  • Discuss high level components of Pig
  • Pig Grunt - How to Start and Use
  • Pig Latin Programming
  • Data Types
  • Cheat sheet
  • Schema
  • Expressions
  • Commands and Exercise
  • Load, Store, Dump, Relational Operations,Foreach, Filter, Group, Order By, Distinct, Join, Cogroup,Union, Cross, Limit, Sample, Parallel
  • Use Cases (working exercise)
  • Use Case 1
  • Use Case 2
  • Use Case 3 (compare pig and hive)
  • Advanced Features, UDFs

Best Practices and common pitfalls

  • Mahout & Machine Learning
  • Mahout Overview
  • Mahout Installation
  • Introduction to the Math Library
  • Vector implementation and Operations (Hands-on exercise)
  • Matrix Implementation and Operations (Hands-on exercise)
  • Anatomy of a Machine Learning Application
  • Classification
  • Introduction to Classification
  • Classification Workflow
  • Feature Extraction
  • Classification Techniques (Hands-on exercise)
  • Evaluation (Hands-on exercise)
  • Clustering
  • Use Cases
  • Clustering algorithms in Mahout
  • K-means clustering (Hands-on exercise)
  • Canopy clustering (Hands-on exercise)
  • Clustering
  • Mixture Models
  • Probabilistic Clustering – Dirichlet (Hands-on exercise)
  • Latent Dirichlet Model (Hands-on exercise)
  • Evaluating and Improving Clustering quality (Hands-on exercise)
  • Distance Measures (Hands-on exercise)
  • Recommendation Systems
  • Overview of Recommendation Systems
  • Use cases
  • Types of Recommendation Systems
  • Collaborative Filtering (Hands-on exercise)
  • Recommendation System Evaluation (Hands-on exercise)
  • Similarity Measures
  • Architecture of Recommendation Systems
  • Wrap Up


Videos for Hadoop Online Training Course

Hadoop training course content and Syllabus

Hadoop Course Content

Hadoop Overview, Architecture Considerations, Infrastructure, Platforms and Automation
Use case walkthrough
ETL
Log Analytics
Real Time Analytics
Hbase for Developers :

  • NoSQL Introduction
  • Traditional RDBMS approach
  • NoSQL introduction
  • Hadoop & Hbase positioning
  • Hbase Introduction
  • What it is, what it is not, its history and common use-cases
  • Hbase Client – Shell, exercise
  • Hbase Architecture
  • Building Components
  • Storage, B+ tree, Log Structured Merge Trees
  • Region Lifecycle
  • Read/Write Path
  • Hbase Schema Design
  • Introduction to hbase schema
  • Column Family, Rows, Cells, Cell timestamp
  • Deletes
  • Exercise - build a schema, load data, query data
  • Hbase Java API – Exercises
  • Connection
  • CRUD API
  • Scan API
  • Filters
  • Counters
  • Hbase MapReduce
  • Hbase Bulk load
  • Hbase Operations, cluster management
  • Performance Tuning
  • Advanced Features
  • Exercise
  • Recap and Q&A
  • MapReduce for Developers

Introduction
  • Traditional Systems / Why Big Data / Why Hadoop
  • Hadoop Basic Concepts/Fundamentals
  • Hadoop in the Enterprise
  • Where Hadoop Fits in the Enterprise
  • Review Use Cases
  • Architecture
  • Hadoop Architecture & Building Blocks
  • HDFS and MapReduce
  • Hadoop CLI
  • Walkthrough
  • Exercise
  • MapReduce Programming
  • Fundamentals
  • Anatomy of MapReduce Job Run
  • Job Monitoring, Scheduling
  • Sample Code Walk Through
  • Hadoop API Walk Through
  • Exercise
  • MapReduce Formats
  • Input Formats, Exercise
  • Output Formats, Exercise
  • Hadoop File Formats

MapReduce Design Considerations

  • MapReduce Algorithms
  • Walkthrough of 2-3 Algorithms
  • MapReduce Features
  • Counters, Exercise
  • Map Side Join, Exercise
  • Reduce Side Join, Exercise
  • Sorting, Exercise
  • Use Case A (Long Exercise)
  • Input Formats, Exercise
  • Output Formats, Exercise
  • MapReduce Testing

  • Hadoop Ecosystem
  • Oozie
  • Flume
  • Sqoop
  • Exercise 1 (Sqoop)
  • Streaming API
  • Exercise 2 (Streaming API)
  • Hcatalog
  • Zookeeper
  • HBase Introduction
  • Introduction
  • HBase Architecture
  • MapReduce Performance Tuning

Development Best Practice and Debugging

Apache Hadoop for Administrators

  • Hadoop Fundamentals and Architecture
  • Why Hadoop, Hadoop Basics and Hadoop Architecture
  • HDFS and Map Reduce
  • Hadoop Ecosystems Overview
  • Hive
  • Hbase
  • ZooKeeper
  • Pig
  • Mahout
  • Flume
  • Sqoop
  • Oozie
  • Hardware and Software requirements
  • Hardware, Operating System and Other Software
  • Management Console
  • Deploy Hadoop ecosystem services
  • Hive
  • ZooKeeper
  • HBase
  • Administration
  • Pig
  • Mahout
  • Mysql
  • Setup Security
  • Enable Security – Configure Users, Groups, Secure HDFS, MapReduce, HBase and Hive
  • Configuring User and Groups
  • Configuring Secure HDFS
  • Configuring Secure MapReduce
  • Configuring Secure HBase and Hive
  • Manage and Monitor your cluster

Command Line Interface

Troubleshooting your cluster

Introduction to Big Data and Hadoop

  • Hadoop Overview
  • Why Hadoop
  • Hadoop Basic Concepts
  • Hadoop Ecosystem – MapReduce, Hadoop Streaming, Hive, Pig, Flume, Sqoop, Hbase, Oozie, Mahout
  • Where Hadoop fits in the Enterprise
  • Review use cases
  • Apache Hive & Pig for Developers

  • Overview of Hadoop
  • Big Data and the Distributed File System
  • MapReduce
  • Hive Introduction
  • Why Hive?
  • Compare vs SQL
  • Use Cases
  • Hive Architecture – Building Blocks
  • Hive CLI and Language (Exercise)
  • HDFS Shell
  • Hive CLI
  • Data Types
  • Hive Cheat-Sheet
  • Data Definition Statements
  • Data Manipulation Statements
  • Select, Views, GroupBy, SortBy/DistributeBy/ClusterBy/OrderBy, Joins
  • Built-in Functions
  • Union, Sub Queries, Sampling, Explain
  • Hive Usecase implementation - (Exercise)
  • Use Case 1
  • Use Case 2
  • Best Practices
  • Advance Features
  • Transform and Map-Reduce Scripts
  • Custom UDF
  • UDTF
  • SerDe
  • Recap and Q&A
  • Pig Introduction
  • Position Pig in Hadoop ecosystem
  • Why Pig and not MapReduce
  • Simple example (slides) comparing Pig and MapReduce
  • Who is using Pig now and what are the main use cases
  • Pig Architecture
  • Discuss high level components of Pig
  • Pig Grunt - How to Start and Use
  • Pig Latin Programming
  • Data Types
  • Cheat sheet
  • Schema
  • Expressions
  • Commands and Exercise
  • Load, Store, Dump, Relational Operations,Foreach, Filter, Group, Order By, Distinct, Join, Cogroup,Union, Cross, Limit, Sample, Parallel
  • Use Cases (working exercise)
  • Use Case 1
  • Use Case 2
  • Use Case 3 (compare pig and hive)
  • Advanced Features, UDFs

Best Practices and common pitfalls

  • Mahout & Machine Learning
  • Mahout Overview
  • Mahout Installation
  • Introduction to the Math Library
  • Vector implementation and Operations (Hands-on exercise)
  • Matrix Implementation and Operations (Hands-on exercise)
  • Anatomy of a Machine Learning Application
  • Classification
  • Introduction to Classification
  • Classification Workflow
  • Feature Extraction
  • Classification Techniques (Hands-on exercise)
  • Evaluation (Hands-on exercise)
  • Clustering
  • Use Cases
  • Clustering algorithms in Mahout
  • K-means clustering (Hands-on exercise)
  • Canopy clustering (Hands-on exercise)
  • Clustering
  • Mixture Models
  • Probabilistic Clustering – Dirichlet (Hands-on exercise)
  • Latent Dirichlet Model (Hands-on exercise)
  • Evaluating and Improving Clustering quality (Hands-on exercise)
  • Distance Measures (Hands-on exercise)
  • Recommendation Systems
  • Overview of Recommendation Systems
  • Use cases
  • Types of Recommendation Systems
  • Collaborative Filtering (Hands-on exercise)
  • Recommendation System Evaluation (Hands-on exercise)
  • Similarity Measures
  • Architecture of Recommendation Systems
  • Wrap Up


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