Big Data Analytics and Hadoop

Course Overview

The ‘Prologue to Big Data Analytics and Hadoop’ is a perfect course bundle for people who need to comprehend the essential ideas of Big Data and Hadoop. On finishing this course, participants will have the capacity to decipher what goes behind the preparing of enormous volumes of information as the business changes over from exceeding expectations based examination to an ongoing investigation.

The course concentrates on the fundamentals of Big Data and Hadoop. It additionally gives an outline of the business dispersions of Hadoop and also the segments of the Hadoop environment.

Course Objective

  • Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
  • Gartner defines Big Data as high volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.
  • According to IBM, 80% of data captured today is unstructured, from sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals, to name a few. All of this unstructured data is Big Data.

Course Outcome

  • Organizations are discovering that important predictions can be made by sorting through and analyzing Big Data.
  • Hadoop is the core platform for structuring Big Data and solves the problem of formatting it for subsequent analytics purposes. Hadoop uses a distributed computing architecture consisting of multiple servers using commodity hardware, making it relatively inexpensive to scale and support extremely large data stores.
  • However, Hadoop is not without its challenges…

Prerequisites

There are no prerequisites required to learn this course,

FAQ’S

What will we get after the course?

Certificate of Training, Lifetime Access to LMS & Job assistance

 

Course Curriculum

  • Course Objective Summary

    • During this course, you will learn
      0m
    • Introduction to Big Data and Hadoop
      0m
    • Hadoop ecosystem – Concepts
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    • Hadoop Map-reduce concepts and features
      0m
    • Developing the map-reduce
      0m
  • Applications

    • Pig concepts
      0m
    • Hive concepts
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    • Oozie workflow concepts
      0m
    • HBASE Concepts
      0m
    • Real Life Use Cases
      0m
  • Introduction to Big Data and Hadoop

    • What is Big Data?
      0m
    • What are the challenges for processing
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    • Big data?
      0m
    • What technologies support big data?
      0m
    • What is Hadoop?, Why Hadoop?, History of Hadoop & Use Cases of Hadoop
      0m
    • Hadoop ecosystem
      0m
    • Explain the Driver, Mapper and
      0m
    • Reducer code
      0m
    • Configuring development environment
      0m
    • Eclipse
      0m
    • Writing Unit Test
      0m
    • Running locally
      0m
    • Running on Cluster
      0m
    • Hands on exercises
      0m
  • How Map-Reduce Works

    • Anatomy of Map Reduce Job run
      0m
    • Job Submission
      0m
    • Job Initialization
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    • Task Assignment
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    • Job Completion
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    • Job Scheduling
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    • Job Failures
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    • Shuffle and sort
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    • Oozie Workflows
      0m
    • Hands on Exercises
      0m
  • Map Reduce Types and Formats

    • Map Reduce Types
      0m
    • Input Formats – Input splits & records,
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    • Text input, binary input, multiple inputs & database input
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    • HDFS
      0m
    • Map Reduce
      0m
    • Statistics
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  • Understanding the Cluster

    • Typical workflow
      0m
    • Writing files to HDFS
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    • Reading files from HDFS
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    • Rack Awareness
      0m
    • 5 daemons
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  • Let's talk Map Reduce

    • Before Map reduce
      0m
    • Map Reduce Overview
      0m
    • Word Count Problem
      0m
    • Word Count Flow and Solution
      0m
    • Map Reduce Flow
      0m
    • Algorithms for simple & Complex problems
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  • Developing the Map Reduce Application

    • Data Types
      0m
    • File format
      0m
    • Output Formats – text Output, binary
      0m
    • output, multiple outputs, lazy output
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    • and database output
      0m
    • Hands on Exercises
      0m
  • Map Reduce Features

    • Counters
      0m
    • Sorting
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    • Joins – Map Side and Reduce Side
      0m
    • Joins – Map Side and Reduce Side
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    • MapReduce Combiner
      0m
    • MapReduce Partitioner
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    • MapReduce Distributed Cache
      0m
    • Hands Exercises
      0m
  • Hive and PIG

    • Fundamentals
      0m
    • When to Use PIG and HIVE
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    • Concepts
      0m
    • Hands on Exercises
      0m
  • HBASE

    • CAP Theorem
      0m
    • Introduction to NOSQL
      0m
    • Hbase Architecture and concepts
      0m
    • Quiz11.1
      Programming and Hands on Exercises
      0 questions
    • Programming and Hands on Exercises
      0m

Instructors

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