HADOOP Training in Bangalore

HADOOP Training in Bangalore & Best HADOOP Training Institutes in Bangalore

Best HADOOP Training in Bangalore

Softgen Infotech located in BTM Layout, Bangalore is a leading training institute providing real-time and placement oriented HADOOP Training Courses in bangalore. Our HADOOP training course includes basic to advanced levels. we have a team of certified trainers who are working professionals with hands on real time HADOOP projects knowledge which will provide you an edge over hadoop training institutes.

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

Our team of experts at Softgen Infotech Training Institute, Bangalore have designed our HADOOP Training course content and syllabus based on students requirements to achieve everyone\'s career goal.  Our HADOOP Training course fee is economical and tailor-made based on training requirement.

We Provide regular training classes(day time classes), weekend training classes, and fast track training classes for HADOOP Training in our centres located across bangalore. We also provide Online Training Classes for HADOOP Training Course.

Contact us today to schedule a free demo and complete course details on HADOOP Training Course.


HADOOP Training Course Duration

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

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HADOOP Training Course Content

Who Should Attend?

  • The course is intended for programmers, architects, and project managers who have to process large amounts of data offline.

 

Benefits of Attendance:

  • Upon completion of this course, students will be able to:
  • Understand basic concepts of MapReduce applications developed using Hadoop
  • Understand how Hadoop works in and supports cloud computing and explore examples with Amazon Web Services and case studies
  • Use Apache Hadoop and write MapReduce programs

 

Prerequisites:

  • Students should have a basic familiarity with Linux administration and Java, as most code examples will be written in Java. Familiarity with basic statistical concepts (e.g. histogram, correlation) is helpful to appreciate the more advanced data processing examples.

 

Course Outline:

What is Hadoop?

  • Understanding distributed systems and Hadoop
  • Comparing SQL databases and Hadoop
  • Understanding MapReduce
  • Counting words with Hadoop—running your first program, History of Hadoop.

Starting Hadoop

  • The building blocks of Hadoop
  • Setting up SSH for a Hadoop cluster
  • Running Hadoop
  • Web-based cluster UI

 

Components of Hadoop

  • Working with files in HDFS
  • Anatomy of a MapReduce program
  • Reading and writing

 

Writing basic MapReduce programs

  • Constructing the basic template of a MapReduce program
  • Counting things
  • Adapting for Hadoop’s API changes
  • Streaming in Hadoop
  • Improving performance with combiners

 

Advanced MapReduce

  • Chaining MapReduce jobs
  • Joining data from different sources
  • Creating a Bloom filter

 

Programming Practices

  • Developing MapReduce programs
  • Monitoring and debugging on a production cluster
  • Tuning for performance

 

Cookbook

  • Passing job-specific parameters to your tasks
  • Probing for task-specific information
  • Partitioning into multiple output files
  • Inputting from and outputting to a database
  • Keeping all output in sorted order

 

Managing Hadoop

  • Setting up parameter values for practical use
  • Checking system’s health
  • Setting permissions
  • Managing quotas
  • Enabling trash
  • Removing DataNodes
  • Adding DataNodes
  • Managing NameNode and Secondary NameNode
  • Recovering from a failed NameNode
  • Designing network layout and rack awareness
  • Scheduling jobs from multiple users

 

Running Hadoop in the cloud

  • Introducing Amazon Web Services
  • Setting up AWS
  • Setting up Hadoop on EC2
  • Running MapReduce programs on EC2
  • Cleaning up and shutting down your EC2 instances
  • Amazon Elastic MapReduce and other AWS services

 

Programming with Pig

  • Installing Pig
  • Running Pig
  • Learning Pig Latin through Grunt
  • Speaking Pig Latin
  • Working with user-defined functions
  • Working with scripts
  • Seeing Pig in action—example of computing similar patents

 

Hadoop Related Technologies

  • Hive
  • Apache Accumulo
  • Other Hadoop-related stuff

 

Case studies

  • Converting 11 million image documents from the New York Times archive
  • Mining data at China Mobile
  • Recommending the best websites at StumbleUpon
  • Building analytics for enterprise search—IBM’s Project ES2