Best Hadoop Admin Training in Bangalore

Hadoop Admin Training in Bangalore and Best Hadoop Admin Training Institute in Bangalore

Hadoop Admin Training in Bangalore

Softgen Infotech is rated as one of the Best HADOOP Training Institutes in Bangalore based on Google and other third party reviews. We have completed over 2000 Training classes in HADOOP and provided 100% placement assistance to students. Over the past 5 years, Team at Softgen Infotech is dedicated to provide High-Quality Training in HADOOP.

Trainers at Softgen Infotech are handpicked from HADOOP Industry and will have a minimum of 5 years of experience in implementing HADOOP Technology. All HADOOP Trainers are Professional real-time working consultants who offer hands on experience based on live scenarios.

Our HADOOP training center in Bangalore is well equipped with lab facilities and excellent infrastructure for providing you real time training experience. We also provide certification training programs in HADOOP.  We have successfully trained and provided placement for many of our students in major MNC Companies, after successful completion of the course. We provide 100% placement support for our students.

HADOOP Training Course Content 

Course Material for HADOOP is designed as per current scenarios and will help students in understanding the HADOOP concept in detail. Our HADOOP Training Syllabus covers all the topics related to HADOOP Technology and gives a clear understanding of live scenario to students. Our HADOOP experts at Softgen Infotech Training Institute, Bangalore have developed a unique way of teaching HADOOP in an efficient manner.

View Detailed HADOOP syllabus and topics covered in HADOOP training in Bangalore in the Syllabus section below

Our HADOOP training is conducted on both weekday and weekends depending on participant’s requirement. We do offer Fast-Track HADOOP Training in Bangalore and One-to-One Training for HADOOP in Bangalore. Every topic will be covered in practical way with real-time examples.

HADOOP Training Course Fee

We Charge nominal amount for HADOOP Training in Bangalore and are committed to provide high-quality training. Course fee for HADOOP is affordable and best price when compared to any other institute with good quality standards.

Flexible and Digital payments options are available for HADOOP Training students. Contact us today to schedule a free demo and get details on HADOOP Training in Bangalore.


Hadoop Admin Training Course Fees

We Charge nominal amount for Hadoop Admin Training in Bangalore and are committed to provide high-quality training. Course fee for Hadoop Admin Training is affordable and best price when compared to any other institute with good quality standards.

Flexible and Digital payments options are available for Hadoop Admin Training students.


Hadoop Admin Training Course Duration & Batch Timings

Duration : 30 Hours Version : latest Version
Regular : 1 Hour per day Fast Track : 2 - 3 Hours per day: 10 days
Weekdays : Monday - Friday Weekend : Saturday and Sunday
Online Training : Available Class Room Training : Available
Course Fee : Talk to our Customer Support Mode of Payment : Talk to our Customer Support

Hadoop Admin Training Trainer Profile

  • 5+ Years of Experience in Industry
  • Real-Time Consultant in Major MNC
  • Has trained over 2000 students
  • Strong Theoretical & Practical Knowledge in Hadoop Admin Training
  • Certified Professionals
  • Hands on experience with live projects
  • One-to-One guidance in practical classes

Hadoop Admin Training and Placement in Bangalore

  • More than 2000+ students Trained
  • 98% percent Placement Record
  • Organized over 2000+ Interviews

Our Hadoop Admin Training Center Located in BTM Layout, Bangalore

Our Training Center Located in BTM Layout, Bangalore is Very Near and Accessible from below locations

  • JP Nagar
  • Koramangala
  • Bannerghatta Road
  • Silk Board
  • Madivala

ENROLL TODAY


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Hadoop Admin Training Reviews

Softgen Infotech

4.9 out of 5based on 2316 ratings.

Live and Interactive Sessions for Hadoop Admin Training
Realtime and Job Oriented Course Content for Hadoop Admin Training
Practical Classes with Well Equipped Lab Facilities for Hadoop Admin Training
Interview Prepration and Placement Assistance for Hadoop Admin Training

Hadoop Admin 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

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