Hadoop Online Training

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MSR Trainings: is a brand and providing quality Hadoop online training with experiance faculty with online support for students and employees in world wide. MSR Trainings providing Best Hadoop online training in Hyderabad, India, USA, UK, Australia, New Zealand, UAE, Saudi Arabia,Pakistan, Singapore, Kuwait

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Course Name: Hadoop Administation and Development
24*7 technical support
Faculty : Realtime Experience
MSR Trainings: is a brand and providing quality online and offline trainings for students in
world wide. MSR Trainings providing Best Hadoop online training in Hyderabad.
Highlights in our training service:
Every faculty has Real Time experience .Trained Resources placed in countries like
India,Australia, USA, UK, JAPAN, SWEDEN Itely,Newzeland,singapor etc.Any critical
issues faced by resource resolved using Teamviewer, webex.Supporting the resource with
Top 100 Interview questions.Resume built in best corporate standards according to the job
description.We will market the resume for top technolgy countries.After each week a status
exam is conducted.offline online trainings are conducted everyday.Weekend trainings for
job goers.flexible timings in accordance with the resource comfortability.If version related to
any Tool is upgraded. We will send the upgraded information via email.we will develop the
Aquintance with Production,development and testing environments.Real time scenarios
covered accross Software Development Life Cycle.for every 10 hours One hour catered to
resolve the doubts.Explaining bugs and critical issues and development activities 24*7
technical supports sevices.
Course Content:
Course Objective Summary
During this course, you will learn:
• Introduction to Big Data and Analytics
• Introduction to Hadoop
• Hadoop ecosystem - Concepts
• Hadoop Map-reduce concepts and features
• Developing the map-reduce Applications
• Pig concepts
• Hive concepts
• Sqoop concepts
• Flume Concepts
• Oozie workflow concepts
• Impala Concepts
• Hue Concepts
• HBASE Concepts

• ZooKeeper Concepts
• Real Life Use Cases
Reporting Tool
• Tableau
1. Virtualbox/VM Ware
• Basics
• Installations
• Backups
• Snapshots
2. Linux
• Basics
• Installations
• Commands
3. Hadoop
• Why Hadoop?
• Scaling
• Distributed Framework
• Hadoop v/s RDBMS
• Brief history of hadoop
4. Setup hadoop
• Pseudo mode
• Cluster mode
• Ipv6
• Ssh
• Installation of java, hadoop
• Configurations of hadoop
• Hadoop Processes ( NN, SNN, JT, DN, TT)
• Temporary directory
• UI
• Common errors when running hadoop cluster, solutions
5. HDFS- Hadoop distributed File System

• HDFS Design and Architecture
• HDFS Concepts
• Interacting HDFS using command line
• Interacting HDFS using Java APIs
• Dataflow
• Blocks
• Replica
6. Hadoop Processes
• Name node
• Secondary name node
• Job tracker
• Task tracker
• Data node
7. Map Reduce
• Developing Map Reduce Application
• Phases in Map Reduce Framework
• Map Reduce Input and Output Formats
• Advanced Concepts
• Sample Applications
• Combiner
8. Joining datasets in Mapreduce jobs
• Map-side join
• Reduce-Side join
9. Map reduce – customization
• Custom Input format class
• Hash Partitioner
• Custom Partitioner
• Sorting techniques
• Custom Output format class
10. Hadoop Programming Languages :I.HIVE

• Introduction
• Installation and Configuration
• Interacting HDFS using HIVE
• Map Reduce Programs through HIVE
• HIVE Commands
• Loading, Filtering, Grouping….
• Data types, Operators…..
• Joins, Groups….
• Sample programs in HIVE
II. PIG
• Basics
• Installation and Configurations
• Commands….
OVERVIEW HADOOP DEVELOPER
11. Introduction
12. The Motivation for Hadoop
• Problems with traditional large-scale systems
• Requirements for a new approach
13. Hadoop: Basic Concepts
• An Overview of Hadoop
• The Hadoop Distributed File System
• Hands-On Exercise
• How MapReduce Works
• Hands-On Exercise
• Anatomy of a Hadoop Cluster
• Other Hadoop Ecosystem Components
14. Writing a MapReduce Program
• The MapReduce Flow
• Examining a Sample MapReduce Program
• Basic MapReduce API Concepts
• The Driver Code

• The Mapper
• The Reducer
• Hadoop’s Streaming API
• Using Eclipse for Rapid Development
• Hands-on exercise
• The New MapReduce API
15. Common MapReduce Algorithms
• Sorting and Searching
• Indexing
• Machine Learning With Mahout
• Term Frequency – Inverse Document Frequency
• Word Co-Occurrence
• Hands-On Exercise.
16.PIG Concepts..
• Data loading in PIG.
• Data Extraction in PIG.
• Data Transformation in PIG.
• Hands on exercise on PIG.
17. Hive Concepts.
• Hive Query Language.
• Alter and Delete in Hive.
• Partition in Hive.
• Indexing.
• Joins in Hive.Unions in hive.
• Industry specific configuration of hive parameters.
• Authentication & Authorization.
• Statistics with Hive.
• Archiving in Hive.
• Hands-on exercise
18. Working with Sqoop
• Introduction.
• Import Data.
• Export Data.
• Sqoop Syntaxs.

• Databases connection.
• Hands-on exercise
19. Working with Flume
• Introduction.
• Configuration and Setup.
• Flume Sink with example.
• Channel.
• Flume Source with example.
• Complex flume architecture.
20. OOZIE Concepts
21. IMPALA Concepts
22. HUE Concepts
23. HBASE Concepts
24. ZooKeeper concepts
Reporting Tool..
Tableau
This course is designed for the beginner to intermediate-level Tableau user. It is for anyone
who works with data – regardless of technical or analytical background. This course is
designed to help you understand the important concepts and techniques used in Tableau to
move from simple to complex visualizations and learn how to combine them in interactive
dashboards.
Course Topics
Overview
• What is visual analysis?
• Strengths/weakness of the visual system.
Laying the Groundwork for Visual Analysis
• Analytical Process
• Preparing for analysis
Getting, Cleaning and Classifying Your Data

• Cleaning, formatting and reshaping.
• Using additional data to support your analysis.
• Data classification
Visual Mapping Techniques
• Visual Variables : Basic Units of Data Visualization
• Working with Color
• Marks in action: Common chart types
Solving Real-World Problems with Visual Analysis
• Getting a Feel for the Data- Exploratory Analysis.
• Making comparisons
• Looking at (co-)Relationships.
• Checking progress.
• Spatial Relationships.
• Try, try again.
Communicating Your Findings
• Fine-tuning for more effective visualization
• Storytelling and guided analytics
• Dashboards
More Details :
http://www.msrtrainings.com/hadoop-online-training
http://msrtrainings.blogspot.in/p/courses.html

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