How Is Big Data and Hadoop Linked?


Big data is an Information Technology term used to describe significantly large amounts of data sets that can be commuted and analysed to reveal associations, trends and patterns. Big data and Hadoop have a symbiotic relationship as big data can only be successfully analysed using a robust framework such as Hadoop.


What is Hadoop and Why Is It Important?

Hadoop is one of the most sought out open source framework that is used primarily for storing data on clusters of low performance systems. This framework is also used to run applications on bulk systems often known as commodity hardware. Hadoop is one of the few frameworks that has the capacity to provide enormous storage for any kind of data. With its significant processing power this framework can ensure that commodity hardware are equipped to handle a series of tasks at the same time, without affecting the performance of these tasks. Since this framework has various practical applications, it is usually learned with the help of a professional Hadoop training course


Key Benefits of Using The Hadoop Framework

One of the key benefits of using Hadoop is its ability to seamlessly integrate the function of using big data on hardware commodities with a high fault tolerance. This ensures that all the tasks are carried out smoothly as the nodes are redistributed in the case of hardware failure. Since this framework automatically makes several copies of the data, the data is not lost due to any hardware failures. Flexibility while using the framework, low cost of the open source framework and scalability to grow the system with time are other key benefits of using Hadoop.


Why Empower Yourself With Hadoop Developer Skills?

As the Hadoop framework has been in demand and might be around for a long time, learning this key skill can give you the cutting edge you require to boost your capabilities as a developer. Reputed companies in the IT Sector often look out for developers who have completed a professional Hadooptraining course and are certified to use the framework in a professional working environment.


Professional Hadoop Training Course Details

The duration of the course is between 25 to 30 hours where in developers will be given both theoretically and practical knowledge. During the course, the instructor will guide the students in a systematic manner with the aim of helping students learn the history of Big Data, Hadoop, the various applications of Hadoop in the IT sector. Extra emphasis will be laid on practical sessions to make sure that students are well prepared to handle any and all challenges that might occur while using this framework at the workplace. Challenges of Hadoop will be explained to students and solutions to combat any challenges while using this framework will also be discussed in detail. At the end of the course, Learners Paradise will certify the students to help them embark on their journey of a successful IT career path.


Keywords Used(Highlighted with Bold and Italic)

Primary Keyword: : Professional Hadoop Training Course (Used Thrice)

Secondary Keyword: : Hadoop Framework (Used Twice)

Tertiary Keyword: Big data and Hadoop (Used twice)

 


HADOOP


Hardware Requirements:-Systems must have at least 2 GB RAM.
Software Requirements:- I will provide all software (Operating System also)

CONTENTS

Virtual box/VM Ware

  • Basics & Installations

Linux

  • Basics & Installations

Hadoop

  • What is Hadoop?
  • Why Hadoop and flow of Hadoop?
  • Scaling?
  • Distributed Framework?
  • Hadoop v/s RDBMS?
  • Brief history of Hadoop?

Hadoop installation in pseudo mode

Hadoop installation in cluster mode

  • Adding and removing nodes (without down time)
  • Decommissioning nodes
  • Block size
  • Hadoop Processes ( NN, SNN, JT, DN, TT)
  • Common errors when running Hadoop cluster, solutions

HDFS- Hadoop distributed File System

  • HDFS Design and Architecture
  • HDFS Concepts
  • Interacting HDFS using command line
  • Dataflow
  • Introduction about Blocks
  • Data Replication
  • Admin Commands
  • Hadoop archives

Hadoop Processes

  • Name node and its functionality
  • Secondary name node and its functionality
  • Job tracker and its functionality
  • Task tracker and its functionality
  • Data node and its functionality
  • Resource manager and its functionality
  • Node manager and its functionality

Map Reduce

  • Developing Map Reduce Application
  • Phases in Map Reduce Framework
  • Map Reduce Input and Output Formats
  • Advanced Concepts
  • Combiner
  • HAR
  • Partitioner, sorting, shuffling
  • Different phases of MapReduce programs
  • Data localization
  • Different unstructured data processing examples
  • Image processing by using MapReduce

Joining datasets in MapReduce jobs

  • Map-side join
  • Reduce-Side join


Hadoop Programming Languages:-

PIG
  • Introduction (Basics)
  • Installation and Configuration
  • Different datatypes in PIG
  • Interacting HDFS using PIG
  • Map Reduce Programs through PIG
  • PIG Commands
  • Execution mechanisms (grunt, script...)
  • Loading, Filtering, Grouping, joins….
  • Sample programs in PIG with Real time

Hive

  • Basics (Introduction)
  • Installation and Configurations
  • Datatypes and operators
  • HQL Commands
  • Interacting HDFS using Hive
  • MapReduce programs through Hive
  • Joins, groups, filter......
  • Sample Programs in hive with real-time
  • Join vs Map Join

Impala

  • Basics
  • Commands

Sqoop

  • Introduction to sqoop
  • Installations & Configurations
  • Sqoop commands
  • Connect to relational database using sqoop and downloading lakhs of records to Hadoop (in single minute)

Flume

  • Basics (Introduction)
  • installation and Configurations

NOSQL Databases Concepts

Hbase

  • Basics & Installations
  • commands
  • Interacting Hbase with HDF

MongoDb

  • Basics & Installations
  • All queries for processing data

OOZIE Introduction

Zookeeper introduction

Apache Spark

  • Introduction
  • Installations and configurations
  • RDD , SC....
  • Scala Introduction
  • Interacting spark with HDFS
  • Programs in Spark through Scala

Highlights

  • Working with Apache & cloudera Hadoop
  • Practical's on Hadoop cluster
  • Real life use cases
  • Will cover old version of Hadoop and latest version of Hadoop

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