Best Hadoop Training in Marathahalli – Whitefield- Bangalore

About Hadoop Training in Bangalore

Bizsmart

4.9 out of 5
based on 2374 ratings.

Bizsmart recognised itself as the leading Hadoop Training Institute in Bangalore. Our Hadoop Training Consultants or Trainers are bestly qualified and Experienced to impart best-quality Hadoop Classroom and Online Training across Bangalore. Bizsmart is recognised pioneer in the field of IT/Non-IT Training in Bangalore. We are mainly focused on revolutionising learning by making it interesting and motivating. we impart range of career oriented trainings for different segments like students, job seekers and professionals. Our team of qualified experts have designed our Hadoop Training course content and syllabus for classroom and Online Training is based on current requirements from the industry. This enables them to be an Industry-Ready Professional, capable of handling majority of the real-world cases. Bizsmart also offer tailor made Hadoop Training trainings for Corporates. Our Hadoop Training in Bangalore is scheduled basically at a time that best suits you, we offer regular training classes (day time classes), weekend training classes, and fast track training classes. Our Hadoop Training course fee is reasonable and tailor-made based on training requirement. Our team will make you confident & comfortable in cracking interviews. We also impart online training through which you can access our tutorial Anywhere, Anytime which is effective and cost-effective. We impart a adaptable interactive environment with dynamic content, e-Learning that not only effectively keeps people up-to-date, but interested as well.  Its a One Stop Shop for all IT and Non IT Training in Marathahalli, Bangalore.For more information and to schedule a free Demo on Hadoop Training, contact Bizsmart @ +91 98865 41264Mode of Training : Classroom / Online Training

Hadoop Training Course Content
Module 1-
Introduction to BigData

What is BigData

how did data become so big

why BigData deserves your attention-

use cases of big data

Different option of analyzing big data.

How can such a huge data are analyzed.

Module 2-
Introduction To Hadoop

What is Hadoop,

History of Hadoop

How Hadoop name was given

Problems with Traditional Large-Scale Systems and Need for
Hadoop

Where Hadoop is being used

Understanding distributed systems and Hadoop

RDBMS and Hadoop

Module 3- Starting
Hadoop

Setup single node hadoop cluster

Configuring Hadoop

Understanding Hadoop Architecture

Understanding Hadoop configuration files

Hadoop Components- HDFS, Map Reduce

Overview of Hadoop Processes

Overview of Hadoop Distributed File System

Name nodes

Data nodes

The Command-Line Interface

The building blocks of Hadoop

Setting up SSH for a Hadoop cluster

Running Hadoop

Web-based cluster UI-NameNode UI, MapReduce UI

Hands-On Exercise: Using HDFS commands

Module 4-
Understanding MapReduce

How MapReduce Works

Data flow in MapReduce

Map operation

Reduce operation

MapReduce Program In JAVA using Eclipse

Counting words with Hadoop—Running your first program

Writing MapReduce Drivers, Mappers and Reducers in Java

Real-world “MapReduce” problems

Hands-On Exercise: Writing a MapReduce Program and Running a
MapReduce Job

Java WordCount Code Walkthrough

Module 5- Hadoop
Ecosystem

Hive

Sqoop

Pig

HBase

Flume

Module 6- Extended
Subjects on Hive

Installing Hive

Introduction to Apache Hive

Getting data into Hive

Hive’s architecture

Hive-HQL

Query execution

Programming Practices and projects in Hive

Troubleshooting

Hands-On Exercise: Hive Programming

Module 7- Extended
Subjects on Sqoop

Installing Sqoop

Configure Sqoop

Import RDBMS data to Hive using Sqoop

Export from to Hive to RDBMS using Sqoop

Hands-On Exercise: Import data from RDBMS to HDFS and Hive

Hands-On Exercise: Export data from HDFS/Hive to RDBM

Module 8- Extended
Subjects on Pig

Introduction to Apache Pig

Install Pig

Pig architecture

Pig Latin – Reading and writing data using Pig

Hands-On Exercise: Programming with pig, Load data, execute
data processing statements.

Module 9- Extended
Subjects on

What is HBase?

Install HBase

HBase Architecture

HBase API

Managing large data sets with HBase

Module 10- setup
multi-node hadoop cluster

Setup multi node hadoop cluster using CentOS dump.

Module 11- flume

Module 12- Advanced
Map/reduce-

Map Reduce API

Combiner, partitioner

Custom Data Types

Input Formats

Output Formats

Common MapReduce Algorithms

Sorting

Searching

Indexing

Module 13- advanced
hadoop concept

Authentication in hadoop,

Administration best practices

Hardware selection for master nodes (NameNode, Job Tracker,
HBase Master)

Hardware selection for slave nodes (Data Nodes, Task
Trackers, and Region Serv­ers)

Cluster growth plan based on storage

Module 14- Summary

Case studies

Sample Applications

References 

Module 15- test

Hadoop Training Interview Questions and Answers

No Interview Questions Found..

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 Training Trainer Profile
Our Hadoop Training Trainers in our Bizsmart

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

Hadoop Training Placements in Bangalore
Hadoop Training Placement through Bizsmart

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

If you are looking for Hadoop Training course in Marathahalli, Whitefield, Varthur, Domlur, AECS Layout, Doddanekundi, Thubarahalli, Nagawara, Nagavara, Banaswadi, HBR Layout, RT Nagar or Hebbal. Please call us or mail your details and our concerned person will get back to you.

Call us on 9632407095
 
CONTACT ME
Call us on 9632407095
 
CONTACT ME
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