Hadoop online training

 Hadoop online training in Hyderabad,India, USA, UK, Australia, New Zealand, UAE, Saudi Arabia,Pakistan, Singapore, Kuwait.

MSR Trainings: is a brand and providing quality Hadoop online training with experiance faculty with online support for students and employees in world wide.

HADOOP Learners can grasp the technology-subject from our highly experienced & certified trainers which will be helping the students And Employees to work in real time projects

MSR Trainings providing Best Hadoop online training in Hyderabad, India, USA, UK, Australia, New Zealand, UAE, Saudi Arabia,Pakistan, Singapore, Kuwait

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 :-


• 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


• Basics
• Installation and Configurations
• Commands….


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..


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


• 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 :






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