Bigdata Hadoop

Best Bigdata Hadoop Training with 2 Real-time Projects
Duration of the Training : 9 weekends

Bigdata Hadoop Syllabus
For whom Hadoop is?

IT folks who want to change their profile in a most demanding technology which is in demand by almost all clients in all domains because of below mentioned reasons-

  • Hadoop is open source (Cost saving / Cheaper)
  • Hadoop solves Big Data problem which is very difficult or impossible to solve using highly paid tools in market
  • It can process Distributed data and no need to store entire data in centralized storage as it is there with other tools.
  • Now a days there is job cut in market in so many existing tools and technologies because clients are moving towards a cheaper and efficient solution in market named HADOOP
  • There will be almost 4.4 million jobs in market on Hadoop by next year.

Please refer below mentioned links:
http://www.computerworld.com/article/2494662/business-intelligence/hadoop-will-be-in-most-advanced-analytics-products-by-2015–gartner-says.html

Can I Learn Hadoop If I Don’t know Java?
Yes,

It is a big myth that if a guy don’t know Java then he can’t learn Hadoop. The truth is that Only Map Reduce framework needs Java except Map Reduce all other components are based on different terms like Hive is similar to SQL, HBase is similar to RDBMS and Pig is script based.
Only MR requires Java but there are so many organizations who started hiring on specific skill set also like HBASE developer or Pig and Hive specific requirements. Knowing MapReuce also is just like become all-rounder in Hadoop for any requirement.

Week-1

  • What is Big Data
  • 3 V’s of Big Data
  • The challenges in market with Big Data problem
  • Problems with existing solutions
  • Why Hadoop is there in market?
  • Significance of Hadoop
  • Hadoop solution for Big Data problem
  • Hadoop Eco System
  • Introduction to HDFS
  • Introduction to Map Reduce
  • 5 Demons of Hadoop
  • Files and Blocks concept
  • Data backup guarantee using Replication factor
  • Who is behind Hadoop?
  • Cloudera Introduction (CDH-3)

Week-2

  • Hadoop Installation and Configuration (CDH-3)
  • Introduction to Read Write operations in file system
  • HDFS Architecture
  • HDFS Shell
  • Data loading Techniques
  • Hadoop Project Environment
  • Map Reduce Overview and Architecture
  • Introduction to Map Reduce jobs
  • Input and Output Format
  • Job Configuration
  • Job Submission
  • Introduction to Eclipse IDE
  • Configuring Hadoop API on Eclipse IDE

Week-3

  • Data loading using Flume
  • Introduction to Pig
  • Pig configuration on Cloudera
  • Why Pig if Map Reduce is there?
  • Analytics using Pig
  • Introduction to Pig Latin
  • Implementation using Pig Script
  • Pig with HDFS
  • Map Reduce jobs for Pig Script
  • Pig usage as Data flow Language
  • Why Yahoo is using Pig instead of Map Reduce
  • Other organization which are using Pig widely
  • Where should we use Pig?
  • Where we can’t use Pig?
  • When it communicates with server
  • How it works on client machine
  • Some real time examples of pig how it is resolving Big Data problem in market
  • Limitations of Pig and solution for that
  • How to create your own Hadoop machine

Week-4

  • Introduction to Hive
  • Why Hive if Map Reduce is there?
  • Analytics using Hive
  • Introduction to Hive QL
  • Difference between Relational Database and Hive
  • Similarities between OLAP and Hive
  • Introduction to Map Reduce (MR-1, Hadoop MR Framework)
  • Why Hive if Pig is there?
  • Hive data types
  • Differences between internal and external tables
  • Where we should use Hive
  • Limitations of Hive
  • Partitioning in Hive
  • Bucketing in Hive
  • Implementation of partitioning and bucketing in Hive
  • Process 5 million records using Hive
  • How it is using commodity hardware and giving Data warehousing features
  • Introduction to Map Reduce Framework
  • How Map Reduce jobs work to execute user’s jobs
  • Why MR is core of Hadoop ecosystem for processing unit

Week-5

    • Hive Architecture
    • How to setup multimode Hadoop cluster
    • Introduction to HBASE
    • NoSQL Databases and HBASE
    • What is the significance of NoSQL
    • NoSQL vs RDBMS
    • Different solutions in Market for NOSQL concept
    • Categories of NOSQL solutions
    • HBASE Architecture
    • Map Reduce first program to understand the processing in MR Framework

Three components of MR program-

    • Mapper
    • Reducer
    • Driver
  • How to configure jobs in Driver
  • Difference between MR-1 and MR-2

Week-6

  • Advanced HBASE
  • Zookeeper Services
  • Hadoop Architecture with HBASE and Zookeeper Service
  • Data import using Sqoop
  • Data Export using Sqoop
  • Limitations in CHD-3
  • Introduction to Distributed Cache and how it helps in processing
  • Input Formats
  • Output Formats
  • Mapside join
  • Reduceside join
  • Introduction to JUnit testing
  • Introduction to MRUnit testing and how it helps
  • How to process Semi structured and Structured data

Week-7

  • How to create your own Custom Format in Map Reduce Framework
  • Hadoop-2.0- New Features
  • Introduction to Apache Oozie
  • How to schedule jobs using Apache Oozie
  • How to combine Pig, Hive, Hbase and MR Framework
  • How we process data in real time by combining several technologies

Week-8

  • Real time examples for Datasets and Analytics using Hadoop
  • Proof of Concept discussion which you will implement after completion of course to get Hadoop Certificate form Radical.
  • More than 200 datasets which are available for your practice purpose before going for Interviews
  • Sample resume to help you creating your resume

Additional Benefits:

  • We provide you Hadoop cluster of 6-12 machines
  • We give you more than 200 datasets references to practice and test your skills on Hadoop
  • We give real time scenarios examples, How to work in real time projects
  • We guide for resume preparation by giving sample resume
  • Will give you 2 POC (proof Of Concept) with Data set so that you can practice before going for interview
  • In 2 months training we provide study material’s soft copy in classroom itself
  • We provide hands –on in class room itself so that you can understand concepts 100%
  • We give assignments for weekdays practice
  • We give you java study links if you want to explore Map Reduce more
  • We conduct Map Reduce in 5th, 6th and 7th week so that you can be familiar with Hadoop before starting MR Framework

Additional Benefits:
Q.1) How Hadoop course will help with boost up my career line?

A. Having an inclination towards understanding how multiple computers work together is enough for you to get into Hadoop. The field of Big Data and Data Analytics is coming up in a big way and if you have knowledge of the tools that are used to process large amounts of data, it will greatly benefit you.

Q.2) Any prior work exp in Hadoop is required?

A. No prior work experience in Hadoop is required.

Q.3) I don’t have Java Background? Can I opt for this program?

A. Definitely you can opt for this program. We also have a “Core Java videos for Hadoop” set of lectures to which you will be given. You will get this core Java videos with the Hadoop Course. Once you join the course.

Q.4) How will be the practical’s done?

A. For your practical work, we will help you set up a Virtual machine in your System. This will be a Local access for you. In case your system doesn’t meet the pre-requisites, we will give you a Remote access to our Cluster for your Practical’s.

Q.5) Will I get 24*7 Support for Java also?

A. Yes, the 24*7 support will be available for ‘ Core Java for Hadoop’ also.

Q.6) What are the system requirements to install Hadoop environment?

A. Your system should have a 3GB RAM, a processor better than core 2 duo. In case, your system falls short of these requirements, we can provide you Remote Access to our Hadoop Cluster.

Q.7) What are the career prospects in Hadoop?

A. There are more than 12000 openings for “Big Data or Hadoop” on Naukari . com however industry is short of trained professionals.