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Course Curriculum

Learning Objectives: In this module, you will understand what Big Data is, the limitations of the traditional solutions for Big Data problems, how Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS, Anatomy of File Read and Write & how MapReduce works.
Introduction to Big Data & Big Data Challenges
Limitations & Solutions of Big Data Architecture
Hadoop & its Features
Hadoop Ecosystem
Hadoop 2.x Core Components
Hadoop Storage: HDFS (Hadoop Distributed File System)
Hadoop Processing: MapReduce Framework
Different Hadoop Distributions
Learning Objectives: In this module, you will learn Hadoop Cluster Architecture, important configuration files of Hadoop Cluster, Data Loading Techniques using Sqoop & Flume, and how to setup Single Node and Multi-Node Hadoop Cluster.
Hadoop 2.x Cluster Architecture
Federation and High Availability Architecture
Typical Production Hadoop Cluster
Hadoop Cluster Modes
Common Hadoop Shell Commands
Hadoop 2.x Configuration Files
Single Node Cluster & Multi-Node Cluster set up
Basic Hadoop Administration
Learning Objectives: In this module, you will understand Hadoop MapReduce framework comprehensively, the working of MapReduce on data stored in HDFS. You will also learn the advanced MapReduce concepts like Input Splits, Combiner & Partitioner.
Traditional way vs MapReduce way
Why MapReduce
YARN Components
YARN Architecture
YARN MapReduce Application Execution Flow
YARN Workflow
Anatomy of MapReduce Program
Input Splits, Relation between Input Splits and HDFS Blocks
MapReduce: Combiner & Partitioner
Demo of Health Care Dataset
Demo of Weather Dataset
Learning Objectives: In this module, you will learn Advanced MapReduce concepts such as Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format and XML parsing.
Distributed Cache
Reduce Join
Custom Input Format
Sequence Input Format
XML file Parsing using MapReduce
Learning Objectives: In this module, you will learn Apache Pig, types of use cases where we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, Pig running modes, Pig UDF, Pig Streaming & Testing Pig Scripts. You will also be working on healthcare dataset.
Introduction to Apache Pig
MapReduce vs Pig
Pig Components & Pig Execution
Pig Data Types & Data Models in Pig
Pig Latin Programs
Shell and Utility Commands
Pig UDF & Pig Streaming
Testing Pig scripts with Punit
Aviation use-case in PIG
Pig Demo of Healthcare Dataset
Learning Objectives: This module will help you in understanding Hive concepts, Hive Data types, loading and querying data in Hive, running hive scripts and Hive UDF.
Introduction to Apache Hive
Hive vs Pig
Hive Architecture and Components
Hive Metastore
Limitations of Hive
Comparison with Traditional Database
Hive Data Types and Data Models
Hive Partition
Hive Bucketing
Hive Tables (Managed Tables and External Tables)
Importing Data
Querying Data & Managing Outputs
Hive Script & Hive UDF
Retail use case in Hive
Hive Demo on Healthcare Dataset
Learning Objectives: In this module, you will understand advanced Apache Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, and optimizations in Hive. You will also acquire in-depth knowledge of Apache HBase, HBase Architecture, HBase running modes and its components.
Hive QL: Joining Tables, Dynamic Partitioning
Custom MapReduce Scripts
Hive Indexes and views
Hive Query Optimizers
Hive Thrift Server
Hive UDF
Apache HBase: Introduction to NoSQL Databases and HBase
HBase v/s RDBMS
HBase Components
HBase Architecture
HBase Run Modes
HBase Configuration
HBase Cluster Deployment
Learning Objectives: This module will cover advance Apache HBase concepts. We will see demos on HBase Bulk Loading & HBase Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster & why HBase uses Zookeeper.
HBase Data Model
HBase Shell
HBase Client API
Hive Data Loading Techniques
Apache Zookeeper Introduction
ZooKeeper Data Model
Zookeeper Service
HBase Bulk Loading
Getting and Inserting Data
HBase Filters
Learning Objectives: In this module, you will learn what is Apache Spark, SparkContext & Spark Ecosystem. You will learn how to work in Resilient Distributed Datasets (RDD) in Apache Spark. You will be running application on Spark Cluster & comparing the performance of MapReduce and Spark.
What is Spark
Spark Ecosystem
Spark Components
What is Scala
Why Scala
Spark RDD
Learning Objectives: In this module, you will understand how multiple Hadoop ecosystem components work together to solve Big Data problems. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.
Oozie Components
Oozie Workflow
Scheduling Jobs with Oozie Scheduler
Demo of Oozie Workflow
Oozie Coordinator
Oozie Commands
Oozie Web Console
Oozie for MapReduce
Combining flow of MapReduce Jobs
Hive in Oozie
Hadoop Project Demo
Hadoop Talend Integration
1) Analyses of a Online Book Store
A. Find out the frequency of books published each year. (Hint: Sample dataset will be provided)
B. Find out in which year maximum number of books were published
C. Find out how many books were published based on ranking in the year 2002.
Sample Dataset Description
The Book-Crossing dataset consists of 3 tables that will be provided to you.
2) Airlines Analysis
A. Find list of Airports operating in the Country India
B. Find the list of Airlines having zero stops
C. List of Airlines operating with code share
D. Which country (or) territory having highest Airports
E. Find the list of Active Airlines in United state
Sample Dataset Description
In this use case, there are 3 data sets. Final_airlines, routes.dat, airports_mod.dat

Course Description

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.

Websoft 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.

Big Data Hadoop Certification Training is designed by industry experts to make you a Certified Big Data Practitioner. The Big Data Hadoop course offers:
In-depth knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) & MapReduce
Comprehensive knowledge of various tools that fall in Hadoop Ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase
The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
The exposure to many real world industry-based projects which will be executed in Edureka’s CloudLab
Projects which are diverse in nature covering various data sets from multiple domains such as banking, telecommunication, social media, insurance, and e-commerce
Rigorous involvement of a Hadoop expert throughout the Big Data Hadoop Training to learn industry standards and best practices

Big Data is one of the accelerating and most promising fields, considering all the technologies available in the IT market today. In order to take benefit of these opportunities, you need a structured training with the latest curriculum as per current industry requirements and best practices.

Besides strong theoretical understanding, you need to work on various real world big data projects using different Big Data and Hadoop tools as a part of solution strategy.

Additionally, you need the guidance of a Hadoop expert who is currently working in the industry on real world Big Data projects and troubleshooting day to day challenges while implementing them.

Big Data Hadoop Certification Training will help you to become a Big Data expert. It will hone your skills by offering you comprehensive knowledge on Hadoop framework, and the required hands-on experience for solving real-time industry-based Big Data projects. During Big Data & Hadoop course you will be trained by our expert instructors to:
Master the concepts of HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), & understand how to work with Hadoop storage & resource management.
Understand MapReduce Framework
Implement complex business solution using MapReduce
Learn data ingestion techniques using Sqoop and Flume
Perform ETL operations & data analytics using Pig and Hive
Implementing Partitioning, Bucketing and Indexing in Hive
Understand HBase, i.e a NoSQL Database in Hadoop, HBase Architecture & Mechanisms
Integrate HBase with Hive
Schedule jobs using Oozie
Implement best practices for Hadoop development
Understand Apache Spark and its Ecosystem
Learn how to work with RDD in Apache Spark
Work on real world Big Data Analytics Project
Work on a real-time Hadoop cluster

The market for Big Data analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Hiring managers are looking for certified Big Data Hadoop professionals. Our Big Data & Hadoop Certification Training helps you to grab this opportunity and accelerate your career. Our Big Data Hadoop Course can be pursued by professional as well as freshers. It is best suited for:

Software Developers, Project Managers
Software Architects
ETL and Data Warehousing Professionals
Data Engineers
Data Analysts & Business Intelligence Professionals
DBAs and DB professionals
Senior IT Professionals
Testing professionals
Mainframe professionals
Graduates looking to build a career in Big Data Field
For pursuing a career in Data Science, knowledge of Big Data, Apache Hadoop & Hadoop tools are necessary. Hadoop practitioners are among the highest paid IT professionals today with salaries ranging around $97K (source: payscale), and their market demand is growing rapidly.
The below predictions will help you in understanding the growth of Big Data:
Hadoop Market is expected to reach $99.31B by 2022 at a CAGR of 42.1% -Forbes
McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
Average Salary of Big Data Hadoop Developers is $97k

Organisations are showing interest in Big Data and are adopting Hadoop to store & analyse it. Hence, the demand for jobs in Big Data and Hadoop is also rising rapidly. If you are interested in pursuing a career in this field, now is the right time to get started with online Hadoop Training.

There are no such prerequisites for Big Data & Hadoop Course. However, prior knowledge of Core Java and SQL will be helpful but is not mandatory. Further, to brush up your skills, Edureka offers a complimentary self-paced course on "Java essentials for Hadoop" when you enroll for the Big Data and Hadoop Course.

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