best hadoop real time training institute in bangalore

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HADOOP FOR DEVELOPERS AND ADMIN COURSE CONTENT
You will learn how to use Apache Hadoop and write MapReduce programs. You will begin with a quick overview of installing Hadoop, setting it up in a cluster, and then proceed to writing data analytic programs. The course will present the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action. The course will further examine related technologies such as Hive, Pig, and Apache Accumulo. Apache Accumulo is a highly scalable structured store based on Google's BigTable, written in Java and operates over the Hadoop Distributed File System (HDFS). Hive is data warehouse software for querying and managing large datasets. Pig is a platform to take advantage of parallelization when running data analysis. Finally, you will observe how Hadoop works in and supports cloud computing and explore examples with Amazon Web Services and case studies.

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

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