Big Data and Hadoop Training

4.0 (7891) 8720 Learners

Get a deeper knowledge of various Big Data frameworks

Hands-on learning on Big data Analytics with Hadoop

Projects related to banking, governmental sectors, e-commerce websites, etc

Learn to extract information with Hadoop MapReduce using HDFS, Pig, Hive, etc.

Upgrade your career in the field of Big data

Duration
30+ hours
Institution
Open Source
Language
English
Video Script
English

Overview

Wissenhive's Big Data Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this online instructor-led Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka's Cloud Lab.

What you will learn

  • Learn the fundamentals
  • Efficient data extraction
  • MapReduce
  • Debugging techniques
  • Hadoop frameworks
  • Real-world analytics

Syllabus

International industry expertise at your disposal as you deep-dive into the research topic and sector of your choice.

Course Content

Introduction to Big Data Hadoop (8 Lectures)

Understanding Big Data

14:51 Play

Types of Big Data

14:51 Play

Difference between Traditional Data and Big Data

14:51 Play

Introduction to Hadoop

14:51 Play

Distributed Data Storage In Hadoop, HDFS and Hbase

14:51 Play

Hadoop Data processing Analyzing Services MapReduce and spark, Hive Pig and Storm

14:51 Play

Data Integration Tools in Hadoop

14:51 Play

Resource Management and cluster management Services

14:51 Play

Big Data Ecosystem (8 Lectures)

Need of Hadoop in Big Data

14:52 Play

Understanding Hadoop And Its Architecture

14:52 Play

The MapReduce Framework

14:52 Play

What is YARN?

14:52 Play

Understanding Big Data Components

14:53 Play

Monitoring, Management and Orchestration Components of Hadoop Ecosystem

14:53 Play

Different Distributions of Hadoop

14:53 Play

Installing Hadoop 3

14:53 Play

Hadoop Cluster Configuration (7 Lectures)

Hortonworks sandbox installation & configuration

16:49 Play

Hadoop Configuration files

16:49 Play

Working with Hadoop services using Ambari

16:49 Play

Hadoop Daemons

16:49 Play

Browsing Hadoop UI consoles

16:49 Play

Basic Hadoop Shell commands

16:49 Play

Eclipse & winscp installation & configurations on VM

16:49 Play

Big Data Processing with MapReduce (7 Lectures)

Running a MapReduce application in MR2

13:24 Play

MapReduce Framework on YARN

13:24 Play

Fault tolerance in YARN

13:24 Play

Map, Reduce & Shuffle phases

13:24 Play

Understanding Mapper, Reducer & Driver classes

13:24 Play

Writing MapReduce WordCount program

13:24 Play

Executing & monitoring a Map Reduce job

13:24 Play

Batch Analytics with Apache Spark (7 Lectures)

SparkSQL and DataFrames

13:25 Play

DataFrames and the SQL API

13:25 Play

DataFrame schema

13:25 Play

Datasets and encoders

13:25 Play

Loading and saving data

13:25 Play

Aggregations

13:25 Play

Joins

13:25 Play

Real Time Analytics with Apache Spark (7 Lectures)

A short introduction to streaming

13:27 Play

Spark Streaming

13:27 Play

Discretized Streams

13:27 Play

Stateful and stateless transformations

13:27 Play

Checkpointing

13:27 Play

Operating with other streaming platforms (such as Apache Kafka)

13:27 Play

Structured Streaming

13:27 Play

Analysing using PIG (10 Lectures)

Background of Pig

13:28 Play

Pig architecture

13:28 Play

Pig Latin basics

13:29 Play

Pig execution modes

13:29 Play

Pig processing – loading and transforming data

13:29 Play

Pig built-in functions

13:29 Play

Filtering, grouping, sorting data

13:29 Play

Relational join operators

13:29 Play

Pig Scripting

13:29 Play

Pig UDF's

13:29 Play

Analyzing using Hive Data Warehousing Infrastructure (12 Lectures)

Background of Hive

13:31 Play

Hive architecture

13:31 Play

Hive Query Language

13:31 Play

Derby to MySQL database

13:31 Play

Managed & external tables

13:31 Play

Data processing – loading data into tables

13:31 Play

Hive Query Language

13:31 Play

Using Hive built-in functions

13:31 Play

Partitioning data using Hive

13:31 Play

Bucketing data

13:31 Play

Hive Scripting

13:31 Play

Using Hive UDF's

13:31 Play

Working With HBase (10 Lectures)

HBase overview

13:33 Play

Data model

13:33 Play

HBase architecture

13:33 Play

HBase shell

13:33 Play

Zookeeper & its role in HBase environment

13:33 Play

HBase Shell environment

13:33 Play

Creating table

13:33 Play

Creating column families

13:33 Play

CLI commands – get, put, delete & scan

13:33 Play

Scan Filter operations

13:33 Play

Importing and Exporting Data using Sqoop (3 Lectures)

Importing data from RDBMS to HDFS

13:34 Play

Exporting data from HDFS to RDBMS

13:34 Play

Importing & exporting data between RDBMS & Hive tables

13:35 Play

Oozie Work Flow Management and using Flume for Analyzing Streaming Data (6 Lectures)

Overview of Oozie

13:37 Play

Oozie Workflow Architecture

13:37 Play

Creating workflows with Oozie

13:37 Play

Introduction to Flume

13:37 Play

Flume Architecture

13:37 Play

Flume Demo

13:37 Play

Visualizing Big Data (4 Lectures)

Introduction

13:38 Play

Tableau

13:38 Play

Chart types

13:38 Play

Data visualization tools

13:38 Play

Introducing Cloud Computing (8 Lectures)

Cloud computing basics

13:39 Play

Concepts and terminology

13:39 Play

Goals and benefits

13:39 Play

Risks and challenges

13:39 Play

Roles and boundaries

13:39 Play

Cloud characteristics

13:39 Play

Cloud delivery models

13:39 Play

Cloud deployment models

13:39 Play

Course Details

Hadoop is an Apache project (i.e. an open source software) to store & process Big Data. Hadoop stores Big Data in a distributed & fault tolerant manner over commodity hardware. Afterwards, Hadoop tools are used to perform parallel data processing over HDFS (Hadoop Distributed File System).

As organisations have realized the benefits of Big Data Analytics, so there is a huge demand for Big Data & Hadoop professionals. Companies are looking for Big data & Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume. 

Wissenhive Hadoop Training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop Ecosystem. This Hadoop developer certification training is stepping stone to your Big Data journey and you will get the opportunity to work on various Big data projects.

Who Should attend?

  • Data Architects
  • Data Scientists
  • Developers
  • BI Developers
  • BI Analysts
  • SAS Developers
  • Data Analysts

Enquiry

Training Options

Self-paced Training

299
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 3 simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support

Live Virtual Classes

499
  • Online Classroom Flexi-Pass
  • Lifetime access 
  • Practice lab and projects with integrated Azure labs
  • Access to Microsoft official content aligned to examination

One on One Training

Enquiry Now
  • Customized learning delivery model (self-paced and/or instructor-led)
  • Flexible pricing options
  • Enterprise grade learning management system (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Exam & Certification

No Exam Required.

you will be required to complete a project which will be assesd by our certified instructors. on succesful completion of the project you will be awarded a training certificate.

Big Data and Hadoop Training

Frequently Asked Questions

The Big Data Analytics course sets you on your path to become an expert in Big Data Analytics by understanding its core concepts and learning the involved technologies. Most of the courses will also involve you working on real-time and industry-based projects. Through an intensive training program, you will learn the practical applications of the field.

Today, the job market is saturated and there is immense competition. Without any specialization, chances are that you will not be considered for the job you are aspiring for.
Big Data Hadoop is used across enterprises in various industries and the demand for Hadoop professionals is bound to increase in the future. Certification is a way of letting recruiters know that you have the right Big Data Hadoop skills they are looking for. With top corporations bombarded with tens of thousands of resumes for a handful of job postings, a Hadoop certification helps you stand out from the crowd. A Certified Hadoop Administrator also commands a higher pay in the market with an average annual income of $123,000. Hadoop certifications can thus propel your career to the next level.

Here are the main differences between Hadoop and Big Data:

  • Accessibility - It is difficult to access Big data, whereas you can use the Hadoop framework for processing and accessing data at a faster rate.
  • Storage - Storing Big Data is extremely difficult as it is usually in structured and unstructured form. Apache Hadoop HDFS can be used to store big data.
  • Significance - Big Data doesn’t have any value on its own until it can be used for creating profit after data processing. Hadoop is the framework that can make Big data meaningful.
  • Definition - Big Data is just a large volume of data present in structured and unstructured form. Hadoop is a framework responsible for handling Big Data and processing it.
  • Developers - Big data developers develop applications in Pg, MapReduce, Spark, Hive, etc. Hadoop developers are responsible for coding that will be used for processing the data.
  • Type - Big data is a problem that doesn’t have any value or meaning unless it is processed. Hadoop is a solution that solves Big Data complex processing.