Big data - Hadoop training Course Outline

This course has been designed to cover all type of audiences spanning from Architect, Administrator to developer. 

In case of any question regarding duration/fees/schedule , do call me @ 9840014739

Module 1 Big data Getting Started	What is Big Data? What is Big Data Analytics ? What  is Apache Hadoop ? History of Hadoop Understanding distributed file systems and Hadoop Hadoop eco system components Hadoop use cases Ubuntu Installation JDK Installation
Module 2 Hadoop Distributed File system	Eclipse Installation Overview of HDFS Communication Protocols Rack Awareness Hadoop cluster Topology Setting up SSH for Hadoop Cluster Running Hadoop –           Pseudo-distributed mode Linux basic commands HDFS file commands Reading and writing to HDFS programmatically
Module 3 MapReduce Framework	Java Basics Anatomy of a MapReduce Program Writables InputFormat OutputFormat Streaming API Inherent failure handling Reading and writing
Module 4 Advanced MapReduce  Programming	Input splits, Record Reader, Mapper, Partition & Shuffle, Reduce, OutputFormat Writing MapReduce program Streaming in Hadoop Counters Performance Tuning Joins Sorting
Module  5 Apache Hadoop Administration	Best Practices for Hadoop setup and infrastructure Hadoop cluster Installation preparation    Ø Cluster network design    Ø  Installation of Linux operating system    Ø  Configuring SSH    Ø  Walkthrough on Rack topology and set up Managing Hadoop cluster    Ø  HDFS cluster management    Ø  Secondary Name node configuration    Ø  Task Tracker management    Ø  Configuring the HDFS quota    Ø  Configuring Fair Scheduler          Ø  Upgrading Hadoop         Ø  Deploying and managing Hadoop          clusters with Ambari Monitoring Hadoop cluster    Ø  Monitoring Hadoop cluster with         Ganglia    Ø  Monitoring Hadoop cluster with               Ambari    Ø  Monitoring Hadoop cluster with Nagia Hadoop Cluster Performance Tuning    Ø  Benchmarking and profiling    Ø  Using compression for input and               output    Ø  Configuring optimal map and reduce         slots  for the TT    Ø  Fine tuning Job Tracker config    Ø  Fine tuning Task Tracker config    Ø  Tuning Shuffle, merge and sort              parameters Security Implementation               Kerberos security mplementation       Workflow Scheduler               Capacity Scheduler                Fair Scheduler    dfsadmin & mradmin commands Administration of Hcatalog and Hive Backup and Recovery Scenario based exercises -          Data node failure & Recovery -          Name Node Failure & Recovery -          JT & TT failure  & Recovery -          Removing data nodes -          Adding Data nodes
Module 6 Pig and Pig Latin	Installation and configuration Running Pig Lating through grunt Writing programs -          Filter , Load & Store functions Writing user defined functions Working with Scripts Lab Exercises
Module 7 HBase and ZooKeeper	NoSQL Vs SQL Cap  Theorem Architecture Installation Configuration Java API MR integration Performance Tuning Lab Exercises
Module 8 Hive	Features of Hive Architecture Installation and configuration HiveQL Lab Exercises
Module 9 Other Hadoop eco system components	Overview of Ambari, Oozie ,Mahout Installing & configuring Sqoop, mysql-server Installing & configuring flume Lab Exercises

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Integrating the map with qlikview

if you guys need any help in integrating the map with qlikview , I can help you out ... 
Do you want to create a compelling Dashboard something as below, I can help you out. 
Pl call me @ 9840014739, I live in Chennai. 

For training related queries , please contact 9790974910

Dcom  Interface made my life easier to integrate R with Qlikview..  You can perform Linear regression, multiple regression analysis, Logistic regression etc.,. 
 I can give cost-effective solution to my customer as whatever statistical analysis I could do with SAS enterprise miner , I can do it at zero cost in R...





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QlikView Business Discovery Platform components

Components of the QlikView Business Discovery Platform

The QlikView Business Discovery platform consists of 3 major components – QlikView
Server, QlikView Publisher and QlikView Desktop, each playing an important part in
designing, developing and implementing almost every QlikView deployment (see Figure 3).
Each component is used primarily by either an IT professional, a business analyst/developer,
or a business user.


QLIKVIEW DESKTOP

The QlikView Desktop is a Windows-based desktop tool that is used by business analysts
and developers to create a data model and to lay out the graphical user interface (GUI or
presentation layer) for QlikView apps. It is within this environment where a developer will use
a SQL-like scripting environment (augmented by ‘wizards’) to create the linkages (connection
strings) to the source data and to transform the data (e.g. rename fields, apply expressions)
so that it can be analyzed and used within the UI, as well as re-used by other QlikView files.
The QlikView Desktop is also the environment where all user interface design and user
experience is developed in a drag-and-drop paradigm: everything from graphs and tables
containing slices of data to multi-tab architectures to application of color scheme templates
and company logos is done here.