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• VU Lab has a very successful track record with 6500 strong alumni working in the IT industry.
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• Our Instructors are certified IT specialists working in fortune 500 companies with a minimum of 15 years experience.
• All training courses are available in both flavours of 3 or 5 day crash courses as well as 30, 60 to 90 day courses.
• All training topics are covered with practical's and our course material is created by Industry Experts and covers latest versions.
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$100K+ jobs need $100K+ talent. Join our SURE CAREER™ Courses below to land in a $100K+ Job.

Java Novice to Professional Training Course Curriculum (VU Lab's Most Popular Course)

Hadoop Novice to Professional Training Course Curriculum (VU Lab's Most Popular Course).

Spring/Hibernate/EJB3 and Web Services Training Course Curriculum (VU Lab's Most Popular Course)

HOT Courses

• Object Oriented Programming
• Core Java (Data Types, Control Statements, Classes, Inheritance, Interfaces, Packages, Multi Threading, Exception Handling, Generics, Annotations, Enums, Networking, I/O, Java Collections Framework)
• JDBC
• Web Development using Java
• Servlets
• Java Server Pages (JSP)
• MVC (Model View Controller) Fundamentals
• Struts MVC
• Spring MVC
• Java Server Faces Introduction
• AJAX Frameworks (DWR, JQUERY, Prototype, Script.aculo.us)
• ANT / MAVEN Java Build Tools
• Log4J Java Logging Tools
• Spring Dependency Injection
• Spring Aspect Oriented Programming
• Spring Transaction Management
• Hibernate Object Relational Mapping Tool (ORM)
• Hibernate integration with Spring
• Unit testing (Junit, TestNG)
• Spring Security (ACEGI Security)
• Spring Remoting Options
• Spring JDBC
• Spring JMS (Point to Point, Pub Sub Models)
• WebServices (Contract First, Contract Last, REST Style WebServices)
• Spring WebServices (Contract First Approach)
• Spring integration with EJB3.0
• EJB3.0 (Stateless Session Beans, Stateful Session Beans, Message Driven Beans)
• Spring best practices in real world.
• Spring
• Hibernate
• Web Services
• SOA
• Adobe Flex with Java
• Android
• HTML5
• Apple Iphone SDK
• Spring Dependency Injection
• Spring Aspect Oriented Programming
• Hibernate Object Relational Mapping Tool (ORM)
• Hibernate integration with Spring
• ANT / MAVEN Java Build Tools
• Log4J Java Logging Tools
• Unit testing (Junit, TestNG)
• Spring Security (ACEGI Security)
• Spring Remoting Options
• Spring JDBC
• Spring Transaction Management
• Spring JMS (Point to Point, Pub Sub Models)
• WebServices (Contract First, Contract Last, REST Style WebServices)
• Spring WebServices (Contract First Approach)
• Spring integration with EJB3.0
• EJB3.0 (Stateless Session Beans, Stateful Session Beans, Message driven Beans
• Spring best practices in real world.

DETAILED HADOOP TRAINING COURSE OBJECTIVES:

• Master in Big Data Science with••••• Hadoop™••••• training.
• Develop expertise in••••• Hadoop 2.0 Distributed File System••••• and••••• MapReduce framework
• Setup a••••• Hadoop Cluster••••• and configure it for real time processing or distributed processing
• Learn the process of data Loading Techniques using••••• Sqoop••••• and••••• Flume. Your RDBMS will become part of your hadoop echo system with reads and writes from hadoop to rdbms or viceversa.
• Program in••••• MapReduce••••• (Both••••• MRv1••••• and••••• MRv2). Actually there is very little difference from the perspective of a developer. You code written in MRv1 will work the same in latest version of Hadoop too.
• Learn to write advanced Map Reduce programs using simple techniques and create••••• workflows.
• Program for Hadoop 2.0 in••••• YARN••••• (MRv2)
• Perform••••• Data Analytics••••• using••••• Pig••••• and••••• Hive. You will love the simplicity of writing••••• Pig scripts••••• to create Map Ruduce jobs.
• Implement••••• HBase, MapReduce Integration, Advanced Usage and••••• Advanced Indexing
• Have an excellent understanding of••••• ZooKeeper service
• Learn the new features instoduced in Hadoop 2.0 -- YARN, HDFS Federation,••••• NameNode High Availability. Again these features will not effect the way your map reduce programs are written. Most of the changes are to make Hadoop runtime more stable for high availability.
• Learn the••••• best Practices for Hadoop••••• Map Reduce job development.
• Implement multiple••••• use cases from real world••••• and solve them using Hadoop jobs.
• All examples will be real world examples covering different domains in the industry like••••• financial, educational, pharmaceutical, fraud detection, energy and space exploration.

Introduction to Hadoop and Big data.

The Motivation for Hadoop

• Problems with Traditional Large-Scale Systems and N-tier web applications
• Introducing Hadoop to answer big questions.
• What problems can be solved with Hadoop.

Hadoop: Basic Concepts and Hadoop Distributed File System (HDFS)

• The Open Source Hadoop Project and various Open Source Hadoop Components and related projects.
• The Hadoop Distributed File System

Introduction to MapReduce

• MapReduce Overview
• Example: WordCount
• Mappers (code walk through of a Java Mapper)
• Reducers (code walk through of a Reducer)

Hadoop Clusters and the Hadoop Ecosystem

• Hadoop Cluster Overview (best practices for cluster configuration and high availability)
• Hadoop Jobs and Tasks (submitting task and tracking tasks)
• Other Hadoop Ecosystem Components (related hadoop open source projects)

Writing a MapReduce Program in Java

• Basic MapReduce API Concepts
• Writing MapReduce Drivers, Mappers, and Reducers in Java
• Speeding Up Hadoop Development by Using Eclipse. Our free Java training will help you to master the concepts of Eclipse IDE.
• Differences Between the Old and New MapReduce APIs. Understanding the future and anticipate changes as hadoop is growing.

Writing a MapReduce Program Using Streaming

• Writing Mappers and Reducers with the Streaming API

Unit Testing MapReduce Programs

• Unit Testing and Quality assurance of Hadoop Jobs.
• The JUnit and MRUnit Testing Frameworks
• Writing Unit Tests with MRUnit
• Running Unit Tests

Digging indepth into the Hadoop API

• Using the ToolRunner Class
• Setting Up and Tearing Down Mappers and Reducers
• Setting Up and Tearing Down Mappers and Reducers

Accessing HDFS Programmatically

• Using The Distributed Cache
• Using the Hadoop API’s Library of Mappers, reducers, and Partitioners

Real world Development Tips and Techniques

• Strategies for Debugging MapReduce Code
• Testing MapReduce Code Locally by Using LocalJobRunner
• Writing and Viewing Log Files
• Retrieving Job Information with Counters
• Reusing Objects
• Creating Map-Only MapReduce Jobs••••• 

Hadoop Partitioners and Reducers

• How Partitioners and Reducers Work Together
• Determining the optimal number of reduces for a job.
• Writing custom partitioners

Data Input and Output and custom formats

• Creating Custom Writable and Writable Comparable Implementations
• Saving Binary Data Using SequenceFile and Avro Data Files
• Issues to Consider When Using File Compression
• Implementing Custom InputFormats and OutputFormats

Common MapReduce Algorithms

• Sorting and Searching Large Data Sets

  Sorting large data requires special handling. Use Apache Solr to search large data sets

• Indexing Data

  Creating Indexes can speed up your hadoop 32x to 64x the normal speed.

• Computing Term Frequency - Inverse•••••

  In computer science, an inverted index is an index data structure storing a mapping from content, such as words or numbers, to its locations in a database file, or in a document or a set of documents.

Course Conclusion

• Expert suggestions to further advance in big data will be provided by instructor.
• Practice test for Vulab Open source Hadoop Certification.

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