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Hadoop Training Institutes in Bangalore

Hadoop Training in Bangalore & Best Hadoop Training Institutes in Bangalore



About Hadoop Training in Bangalore

MyClass Training Bangalore is a Leading Hadoop Training institute in Bangalore providing Real-Time and Placement Oriented Hadoop Training Courses. Our Training Institutes are mainly focussed on introducing new methods of Learning by making it Interesting and Motivating. Our Hadoop Training Centres spans across all major locations in Bangalore. We provide range of Career oriented courses for different segments like students, job seekers and corporate users. 

MyClass Training Institute has distinguished itself as the leading Hadoop Training Institutes in Bangalore. Our Hadoop Consultants or Trainers are highly qualified and experienced working professionals with minimum of 6 Years of hands on real time expertise to deliver high-quality Hadoop Training across Bangalore. Our Hadoop training course includes basic to advanced level. 

Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. In our Hadoop Training you will learn Hadoop Introduction, Hadoop Architecture, Hadoop Configuration, Hadoop Deployment, MapReduce, Advanced HDFS, PIG, Hive, Hbase, Commercial Distribution of Hadoop, Troubleshooting, and Security

Our team of Expert Trainers have designed Hadoop training course content and syllabus as per the current Industry Requirements. This enables our Students to be an Industry-Ready Professionals, capable of handling majority of the real-world ScenarHadoop after Successful completion of the Course.  

We Provide Exclusive Course Materials, Interview Questions, Real Time Project ScenarHadoop on Hadoop Training which will give our students an edge over other Training Institutes. You can Experience Real-time training in our well equipped labs to excel in Hadoop course. Through our associate Training Institutes we have trained more than 800+ Students in Hadoop Training. We Provide day time classes, weekend training classes, evening batch classes and fast track training classes for Hadoop Training. Our Hadoop Training course fee is very economical and tailor made as per Student Requirement.

To Kick Start your Career, Enroll for a free Demo on Hadoop Training @ MyClass Training Today!.

Hadoop 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


Hadoop Training Duration in Bangalore

Regular Classes( Morning, Day time & Evening)
  • Duration : 30 Days
Weekend Training Classes( Saturday, Sunday & Holidays)
  • Duration : 8 Weeks
Fast Track Training Program( 5+ hours daily)
  • Duration : Within 10 days

Hadoop Trainer Profile

Our Hadoop Trainers in our MyClass Training Bangalore Center
  • Has more than 8 Years of Experience.
  • Has worked on 3 realtime Hadoop projects
  • Is Working in a MNC company in Bangalore
  • Already trained 60+ Students so far.
  • Has strong Theoretical & Practical Knowledge

Hadoop Placements in Bangalore

Hadoop Placement through MyClass Training Bangalore Center
  • More than 500+ students Trained
  • 87% percent Placement Record
  • 427 Interviews Organized
  • Hadoop training in Multiple Locations across Bangalore

MyClass Advantage

  • Real Time Trainers
  • 100% Placement
  • Small Training Batch
  • Flexible Timings
  • Excellent Lab Facility
  • Practical Guidance
  • Hands on Experience
  • Certification Support
  •   Multiple Training Locations

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