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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
Hadoop training duration in Gurgaon
Regular Classes( Morning, Day time & Evening)
•             Duration : 6 weeks
Weekend Training Classes( Saturday, Sunday & Holidays)
•             Duration : 10 Weeks
Fast Track Training Program( 6+ hours classes daily)
•             Duration : within 6 weeks
Hadoop trainer Profile & Placement
Our Hadoop Trainers
•             More than 4 Years of experience in Hadoop Technologies
•             Has worked on 4 realtime Hadoop projects
•             Working in a MNC company in Gurgaon
•             Trained 1903+ Students so far.
•             Strong Theoretical & Practical Knowledge
•             Hadoop certified Professionals
Hadoop (hadoop administration) Placement Training in Gurgaon
•             More than 1903+ students Trained
•             1499 students Placed
•             892 Interviews Organized
•             Placement Supported by InterviewDesk.com
 
 

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