Practical Hadoop Big Data Training Course by Certified Architect. Real time hadoop project on how you implement hadoop based projects for Insurance and Financial domain clients. Also includes live project exp discussion , case study discussion and how to position yourself as hadoop /bigdata consultant. Core Java and Linux basics are covered for appropriate persons.

1. Practical Hadoop Big Data Training Course
Course Details:
http://onlinetraining2011.blogspot.com/2012/12/apache-hadoop-and-aws-mapreduce-training.html
Sample Session:
Hadoop Installation lab: (3000 + Youtube Hits)
http://www.youtube.com/watch?v=i9yckEduQBE
Hadoop HDFS File system Lab:
http://www.youtube.com/watch?v=Pp8SV50S9HM
Case Study: Insurance Domain
http://www.linkedin.com/groups/Insurance-Company-Case-Study-Hadoop4838165.S.256068004?qid=19f108a9-f563-4f99-9287-c19a1375ecf4&trk=groups_most_recent-0-bttl&goback=%2Egmr_4838165
LinkedIn-Group ( real time discussion)
Please join linked in group for regular updates on my learning in Hadoop / Bigdata Real time work.
http://www.linkedin.com/groups/Online-Hadoop-Training-4838165
Introduction to HADOOP
Distributed computing , cloud computing
Big data Basics and Need for Parallel Processing
How Hadoop works ?
Introduction to HDFS and Map Reduce
Hadoop Architecture Details
Name Node
Data Node
Secondary Name
Job Tracker
Task Tracker
Node
HDFS ( Hadoop - Distributed File System)
Hadoop Distributed
Data Replication
Data Storage
Data Retrieval
file system , Background, GFS

2. Additional
HDFS commands
MapReduce Programming
MapReduce, Background
Writing MapReduce Programs
Writable and WritableComparable
Input Format, Output Format
Input Split and Block size
Combiner
Partitioner
Number of Mappers and Reducers
Counters
Map Reduce Algorithms and Exercises
Line Count and Word Count
Distributed Search
Sorting Data – Key Value Data Type
Mathematical Transformation example
Working with Counters exercise
Distributed Cache exercise
Zero Reducer based exercises
Hadoop Streaming
Introduction to Hadoop Streaming
Streaming API details and use cases
Python Based Example for Streaming API
Exercise for Hadoop Streaming ( XML Files
Exercises on Ruby
Exercise on C# using MS-Azure.
) Based.
Apache Pig
Installation
Execution Types
Grunt Shell
Pig Latin
Data Processing
Loading and Storing
Data Filtering
Grouping & Joining Operations
Hands on Exercises
Apache HBase Installation and Details
HBase and NOSQL Introduction
HBase Installation and Configuration.

3. HBase and Java Based integration
HBase Hadoop Integration Details.
Hbase basic exercises
Apache Hive Instalaltion and Details
Hive Installation on Single cluster Hadoop
Hive Services
Hive Shell Description
Hive Server·
Meta store Details
Hive QL Basics
Working with Tables, Databases etc.
Hive JDBC programming
Hands on Exercises and Assignments
Node.
Introduction to Amazon Map Reduce (AWS-EMR)
Hadoop using Amaozon Web Service
AWS MapReduce and EC2
AWS - S3 Service Model.
AWS-MR Architecture.
Streaming Exercise using EMR JobFlow.
Hadoop Infrastructure Planning
Basic Hadoop hardware and software req
Small , Medium and Large cluster
Networking challenges in Hadoop Deployment
Disaster Recovery ( DR ) in Hadoop .
Performance Tuning a large cluster
Hadoop Industry Solutions
EMC GreenPlum Introduction
IBM BigInsight Details
Oracle , Microsoft etc Hadoop Offerings
Cloudera and HortonWorks Hadoop Package
Hadoop and Cloud Computing
Using Cloud technologies for distributed
Hadoop on Amazon Web Service.
Hadoop in Oracle Cloud / RackSpace
processing

4. ============================================================
Medium: GotoMeeting
Duration: 40 Hours
Features:
- (40 Hours Live and recorded sessions) +
- 2 Certification Set Papers+
- 1 Live project Case Study+
- 30 + Exercises on Hadoop, MR, Pig, Hive, Hbase etc.
- Live Linkedin discussions/Project support.
- Resume Preparation.
- Virtual Machine for Practice( Linux and Java Installed).
- Java help (if needed)