Hadoop Training

For Attending Big Data Training Session

Upcoming Batch
March/April 2016

Call us : 480-823-3203
email : training@windsorconsulting.net

Training Details

  • Duration

5 weekends

  • Location

At your convenience thru in-person/Online

  • Course

Big Data Hadoop Traininge
Job oriented course

  • Faculty

Expert from the Industry. Working in a leading institution

  • Cost

In Person - $899 and Webex - $375. $350 discount for the first 5 registration. There are referral discounts, please contact us for more information. Registration fee will be $50 and it is refundable before the first session if customer decline to continue.

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Benefits of Hadoop/Data Scientist Program

  • Hadoop is storing and processing very large amount of enterprise data.
  • Apache Hadoop to build powerful applications to analyze Big Data
  • Companies are going towards digital and currently it is a revolution everywhere. There are tens and thousands of job is coming up.
  • Giants such as Google, Yahoo, Apple, eBay, Facebook, ORACLE, IBM, Microsoft, Linkedin and Amazon looking for Hadoop professionals.
  • Researchers predicts that Big Data revenue market will grow at 35% a year and it might hit $35 billion by end of 2016


Introduction: (Day #1)

  • Why Big Data and Evolution of Hadoop
  • What is Hadoop
    • HDFS
    • YARN
    • Map Reduce/Tez
  • HDFS Master-Slave Architecture
    • Namenode
    • Datanode
    • Secondary Namenode
  • Map reduce Master-Slave Architecture
    • Job Tracker
    • Task Tracker
    • YARN Components
  • Benefits of Hadoop
  • Hadoop Ecosystem and other Evolving technologies
  • Realtime Hadoop Cluster architecture
  • Hadoop Use Cases

HDFS: (Day #2)

  • Why Big Data and Evolution of Hadoop
  • What is Hadoop
  • Benefits of Hadoop
  • Hadoop Ecosystem and other Evolving technologies
  • Anatomy of File Read and File Write operations in HDFS
  • DistCp
  • Hadoop Archive filesystem(HAR)
  • Compression in HDFS
  • Serialization in Hadoop

Map Reduce: (Day 3)

  • Map Reduce Evolution and Architecture
  • Anatomy of Mapreduce
    • Mapper
    • Reducer
    • Partitioner
    • Combiner
  • Different modes of running Mapreduce program
  • Shuffle & Sort
  • Decomposing a problem into Mapreduce work flow

Resource Management in Hadoop (Day 4)

  • Classic Mapreduce vs YARN
  • Advantages of YARN
  • Multi Tenancy in YARN
  • How failures are handled in YARN
  • Different schedulers (Fair Scheduler vs Capacity Scheduler)
  • REST API in Capacity Scheduler

Pig: (Day 5 & Day 6)

  • Why Pig and Evolution of Pig
  • Pig Grunt
  • Pig Latin
  • UDFs in Pig
  • Processing Data in Pig
  • Hands On
  • Parallelism in Pig
  • Parameter Substitution in Pig
  • Advanced topics in Pig
  • Hands on Use case with Pig

Hive: (Day 7 & 8)

  • Why Hive and Evolution of Hive
  • Difference between RDBMS & Hive
  • HiveQL
  • Different types of Tables in Hive
  • Partitions and Buckets
  • Storage Formats in Hive
  • Playing with data in Hive
  • MetaData - Altering and Dropping tables in Hive
  • UDFs and UDAFs in Hive
  • Hive running on Tez
  • Hands on use case with Hive

Other Ecosystems in Hive: (Day 9 and Conclusion)

  • Hbase overview
  • Impala overview
  • Spark overview
  • Zookeeper overview
  • Flume Overview
  • Sqoop overview
  • Interview preparation

Map Reduce for Java Developers (Day 10)

  • InputFormat, OutputFormat
  • Counters
  • Advance Mapreduce
    • Joins - Mapside Joins
    • Reduce side Joins
    • Sorting
  • Hands on Map Reduce programming
    • Packaging the jar
    • Launching the job
    • Web UI
    • Debugging the Job
    • Hadoop logs
  • Tuning tips for Mapreduce
  • Project- Hands on


Work on a Real time Project on Big Data Analytics and gain Hands on Project Experience.