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

 

Hadoop training duration in mumbai

Regular Classes( Morning, Day time & Evening)

• Duration : 6 weeks

Weekend Training Classes( Saturday, Sunday & Holidays)

• Duration : 7 Weeks

Fast Track Training Program( 4+ hours classes daily)

• Duration : within 4 weeks

 

Hadoop trainer Profile & Placement

Our Hadoop Trainers

• More than 9 Years of experience in Hadoop Technologies
• Has worked on 9 realtime Hadoop projects
• Working in a MNC company in mumbai
• Trained 153+ Students so far.
• Strong Theoretical & Practical Knowledge
• Hadoop certified Professionals

Hadoop (hadoop administration) Placement Training in mumbai

• More than 153+ students Trained
• 93 percent Placement Record
• 79 Interviews Organized
• Placement Supported by InterviewDesk.com

 

Hadoop training Locations in mumbai

Our Hadoop Bigdata Administration Training centers

• Ballygunge
• Bamangachi
• Bangur Avenue
• Barasat
• Barrackpore
• Belgharia
• Chandannagar
• Chittaranjan Avenue
• Dumdum
• Gangapur
• Garia
• Gariahat Road
• Gopalpur
• Habra
• Hoogly
• Howrah
• Ichapur
• Jadavpur
• Kalani
• Kanchrapara
• Khardaha
• Lake Town
• Madhyamgram
• Naktala
• Park Street
• Rajpur
• Salt Lake City
• Santoshpur
• Sector-1
• Sonapur
• Ultadanga

 

Hadoop training batch size in mumbai

Regular Batch ( Morning, Day time & Evening)

• Seats Available : 5 (maximum)

Weekend Training Batch( Saturday, Sunday & Holidays)

• Seats Available : 4 (maximum)

Fast Track batch

• Seats Available : 5 (maximum)