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 organizations 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.

itToolsTraining 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.

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, our course covers the "Java" Topics as the first module.

• 1.1 - Introductry Elements, Variable, Array, Methods & Classes, Objects
• 1.2 - Alternation & Loops
• 1.3 - Method Overloading, Method Overriding, Abstract Class, Interface, Inheritance
• 1.4 - Inputs, ArrayList, HashMap, Exception Handling

• 2.1 - Big Data Introduction
• 2.2 - Limitations and Solutions of existing Data Analytics Architecture
• 2.3 - Hadoop Introduction, Hadoop Features
• 2.4 - Hadoop Ecosystem, Hadoop 2.x core components, Hadoop Different Distributions
• 2.5 - Hadoop Storage: HDFS Introduction, Hadoop Processing: MapReduce Framework

• 3.1 - Hadoop Shell Commands- LINUX & HDFS
• 3.2 - Hadoop 2.x Architecture, Hadoop Federation
• 3.3 - High Availability Concepts, Different Hadoop Cluster Modes
• 3.4 - Hadoop Cluster Demo, Hadoop 2.x Configuration Files Details
• 3.5 - Single node cluster and Multi node cluster set up

• 4.1 - MapReduce UseCases, Traditional methods Vs MapReduce
• 4.2 - Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components
• 4.3 - YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program
• 4.4 - Input Splits, Relation between Input Splits and HDFS Blocks
• 4.5 - Demo on MapReduce
• 4.6 - MapReduce: Combiner & Partitioner
• 4.7 - Case Studies: Word Count, Health Care Data set, Weather Data set

• 5.1 - About Pig, MapReduce Vs Pig, Pig Use Cases
• 5.2 - Programming Structure in Pig, Pig Running Modes, Pig components
• 5.3 - Pig Execution, Pig Latin Program, Pig Data Types
• 5.4 - Data Models in Pig
• 5.5 - Shell and Utility Commands, Pig Latin : Relational Operators, Pig Latin : File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union
• 5.6 - Lab, WordCount example in PIG
• 5.7 - Pig Latin : Diagnostic Operators, Specialized joins in Pig, Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function), Pig UDF
• 5.8 - Case Studies: HealthCare DataSet Problem Solving

• 6.1 - Hive Introduction, Hive Use Case, About Hive, Hive Vs Pig
• 6.2 - Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database
• 6.3 - Hive Data Types and Data Models, Partitions and Buckets
• 6.4 - Hive Tables(Managed Tables and External Tables), Importing Data, Querying Data, Lab
• 6.5 - Hive Script, Hive UDF, Joins, Lab
• 6.6 - Case Studies: HealthCare DataSet Problem Solving

• 7.1 - Hive QL: Dynamic Partitioning, Thrift Server
• 7.2 - Hive QL: Hive Indexes and views
• 7.3 - HBase: Introduction to NoSQL Databases and HBase
• 7.4 - HBase v/s RDBMS, HBase Components
• 7.5 - Run Modes & Configuration, HBase Architecture

• 8.1 - HBase Data Model, HBase Shell Demo: Getting and Inserting Data, Filters in Hbase
• 8.2 - HBase Client API
• 8.3 - Data Loading Techniques, Bulk Loading, ZooKeeper Data Model, Zookeeper Service, Zookeeper & Hbase
• 8.4 - Case Studies: Zookeeper Hbase Use Case, Practice codes

• 9.1 - Apache Spark Introduction, Spark Ecosystem, Spark Components, History of Spark and Spark Versions/Releases
• 9.2 - What is Scala?, Why Scala?, Scala REPL
• 9.3 - Spark Lab
• 9.4 - SparkContext, RDD
• 9.5 - Case Studies: WordCount on Standalone, Local & HDFS mode

• 10.1 - Flume Introduction & Demo Lab
• 10.2 - Sqoop Introduction & Demo Lab

• 11.1 - Oozie Introduction, Oozie Components, Oozie Workflow, Oozie Web Console
• 11.2 - Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands
• 11.3 - Hadoop Project Description
• 11.4 - Case Studies: Oozie Practicals

As soon as you enroll into the course, your LMS (Learning Management System) access will be functional. You will immediately get access to our course content in the form of a complete set of Videos, Quiz and Assignments. You can start learning right away. 

You can pay by Credit Card, Debit Card or NetBanking from all the leading banks. We use a CCAvenue Payment Gateway.

All the instructors at itToolsTraining are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by itToolsTraining for providing an awesome learning experience.

You can contact us at support@itToolsTraining.com or use contactUs form from our website. 

We do help in preparing for the interviews. Our courses are designed in the way that you can crack the interviews easily provided you have completed the course with assignments and Quizzes.