Bigdata Hadoop Online and ClassRoom Training

what is Hadoop?
 Hadoop is an open source framework used to store and process big data in a group of computers using simple modules. It is made to connect one servers to thousands of systems with individual computation and storage.
what is Big data?
Big Data is an accumulation of large datasets that can't be processed using traditional computing techniques. It is not just a single technique or perhaps a tool, rather it involves many regions of business and technology. Hadoop is not a tool, it is not just a single technique and involves in business and technology regions. Hadoop Training in Hyderabad.

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  Hadoop Training

 Due to the arrival of latest technologies,gadgets,electronics and transmission sources like social networking sites the data generated is increasing every year. The amount of data generated from starting to 2003 was 5 billion GB. If you pile up the information in the proper execution of disks it could fill a whole football field. The same amount was created in every two days in 2011, and in every ten minutes in 2013. This rate is still growing enormously. Details generated is useful but when processing it is been neglected.

Factors comes under Big Data?
 Big Data generated by devices and applications Following factors that comes under Big Data are:

Black Box Data : It is a part of helicopter, airplanes, and jets, etc. Capturing voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.

Social Media Data : Facebook, twitter are the two major social networking sites stores millions of information and views posted by people around the world.

Stock Exchange Data : The stock exchange data holds information about 'buy'and 'sell'decisions of a share of different companies created by the customers.

Power Grid Data : Power consumed with respect to power generation data at a power grid.

Transport Data : Transport data includes model, capacity, distance and availability of a vehicle.

Search Engine Data : Search engines stores a lot of data from various databases
Big Data

Thus Big Data includes huge volume, high velocity, and extensible selection of data. Big Data is of three types they are:
Structured data : Relational data.
Semi Structured data : XML data.
Unstructured data : Word, PDF, Text, Media Logs.
Benefits of Big Data Training
Using the data kept in the social network like Facebook, the marketing agencies are researching the response for their campaigns, promotions, and other advertising mediums.


 Depending upon the preferences and product perceptions in social networking data companies and retailers organize their production.

Hadoop Training in Madhapur

About Hadoop

Hadoop is not unable to process an enormous hunk of information in a lesser time which enabled the firms to assess this was impossible before within that time that is stipulated.

One other significant benefit of the Hadoop programs is the cost effectiveness, which can't be availed in any technologies. You can avoid the fees that must be updated occasionally when using anything as well as the high price active in the software licenses. It's recommended for companies, which may have to work with enormous quantity of information, to select Hadoop programs in repairing any problems, as it will help. Along with these two main elements, there are nine other parts, which are determined in addition to other complementary tools one uses according to the distribution. You'll find three most typical functions of Hadoop programs. 

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Hadoop training

BigData Hadoop Training in Hyderabad

Map Reduce in BigData Hadoop

MapReduce has existed for quite a while, but until and unless the underlying theories which forms the base of MapReduce are understood, an individual will be unable to learn more about range and the real possibility which MapReduce seeks to offer.

Among the main aspects of ensuring that you simply learn Hadoop in a holistic way would be to comprehend the essential theories which form the foundation of the modern world occurrence. Just once these theories have already been well understood, you can move ahead to comprehend the specific dynamics itself.

The facet of MapReduce continues to be present for quite a while and continues to be executed in a variety of scenarios in organizations all over the world, but, the real possibility of the applications is unable to be used until the specific dynamics as well as the theories which form the essential of the applications are well comprehended. Therefore, by preventing to jump in the execution facet of MapReduce, of imparting knowledge related to helping individuals learn Hadoop training in Hyderabad  our political orientation is distinct, yet surprisingly high consequence giving at the same time.

Yet, in addition to the comprehension the theories there are several other facets which playan important part in comprehending the dynamics. The problem statement is included by among the facets. 

All the features are well considered and efficiently produced in Hadoop's batch training. We ensure that Hadoop's dynamics are described in in-depth, to this kind of holistic amount the users can make great usage of the info and begin efficiently in handling the big data set easily, using the notions of MapReduce. Learn Hadoop and comprehend diversification, extent and the real possibility that MapReduce provides for you.

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Hadoop training in hyderabad

Hadoop online training in hyderabad

                       Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance. Rather than relying on high-end hardware, the resiliency of these clusters comes from the software’s ability to detect and handle failures at the application layer.

      Hadoop is supplemented by an ecosystem of Apache projects, such as Pig, Hive and Zookeeper, that extend the value of Hadoop and improves its usability.

Hadoop enables a computing solution that is:

Scalable– New nodes can be added as needed, and added without needing to change data formats, how data is loaded, how jobs are written, or the applications on top.

.Cost effective– Hadoop brings massively parallel computing to commodity servers. The result is a sizeable decrease in the cost per terabyte of storage, which in turn makes it affordable to model all your data

Flexible– Hadoop is schema-less, and can absorb any type of data, structured or not, from any number of sources. Data from multiple sources can be joined and aggregated in arbitrary ways enabling deeper analyses than any one system can provide

Fault tolerant– When you lose a node, the system redirects work to another location of the data and continues processing without missing a fright beat.

Rs Training's : is a brand and providing quality online and offline training's for students in world wide. Rs Training's providing Best Hadoop online training in Hyderabad.

RS TRAINING'S: Interactive training from Technological mavens to groom learners into technological aces.

RS TRAINING'S is an outstanding ONLINE IT TRAINING and CLASSROOM IT TRAINING institute with State of Art infrastructure led by the finest trainers in the market. We offer Online training to the learners in all parts of the world with the implementation of modern technologies like Gotomeeting and Web Ex.

    Our trainers are Domain experts with a proven experience of at least a decade in the real time environment. We believe in the policy that “Learning is the virtue of success in life”.The flexible course curriculum designed at RS TRAINING'S will be up to date in the technological race and be able to cater the requirement of both fresher’s and Professionals. Be it Corporate training or Online Training or Classroom Training for Hadoop ,RS TRAINING'S is elite and provides accomplished training services to cater the client needs.

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