This Hadoop training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop ecosystem and best practices about HDFS, MapReduce, HBase, Hive, Pig, Oozie, Sqoop. This course is stepping stone to your Big Data journey and you will get the opportunity to work on a Big data Analytics project after selecting a data-set of your choice. You will get Addilearn Hadoop certification after the project completion.

The AddiLearn hadoop training is designed to help you become a top Hadoop developer. During this course, our expert instructors will train you to- 

• Master the concepts of HDFS and MapReduce framework
• Understand Hadoop 2.x Architecture
• Setup Hadoop Cluster and write Complex MapReduce programs
• Learn data loading techniques using Sqoop and Flume
• Perform data analytics using Pig, Hive and YARN
• Implement HBase and MapReduce integration
• Implement Advanced Usage and Indexing
• Schedule jobs using Oozie
• Implement best practices for Hadoop development
• Understand Spark and its Ecosystem
• Learn how to work in RDD in Spark
• Work on a real life Project on Big Data Analytics

Big Data & Hadoop Market is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 - Forbes

McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts - Mckinsey Report

Avg salary of Big Data Hadoop Developers is $135k - Indeed.com Salary Data.

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. 
Here are the few Professional IT groups, who are continuously enjoying the benefits moving into Big data domain:

• Developers and Architects
• BI /ETL/DW professionals
• Senior IT Professionals
• Testing professionals
• Mainframe professionals
• Freshers

Hadoop practitioners are among the highest paid IT professionals today with salaries ranging till $85K (source: indeed job portal), and the market demand for them is growing rapidly.

You can check a blog related to Why Choose Hadoop As a Career? Also, once your Hadoop training is over, you can check the Top interview questions related Addilearn blog.

Real-time Analytics is the new market buzz and having Apache Spark skills is a highly preferred learning path after the Hadoop training. 

As such, there are no pre-requisites for learning Hadoop. Knowledge of Core Java and SQL will be beneficial, but certainly not a mandate. If you wish to brush-up Core-Java skills, Addilearn offer you a complimentary self-paced course, i.e. "Java essentials for Hadoop" when you enroll in Big Data Hadoop Certification course.

Learning Objectives : In this module, you will understand Big Data, the limitations of the existing solutions for Big Data problem, how Hadoop solves the Big Data problem, the common Hadoop ecosystem components, Hadoop Architecture, HDFS, Anatomy of File Write and Read, how MapReduce Framework works.

Topics : Big Data, Limitations and Solutions of existing Data Analytics Architecture, Hadoop, Hadoop Features, Hadoop Ecosystem, Hadoop 2.x core components, Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework, Hadoop Different Distributions.

Learning Objectives :In this module, you will learn the Hadoop Cluster Architecture, Important Configuration files in a Hadoop Cluster, Data Loading Techniques, how to setup single node and multi node hadoop cluster. Topics-Hadoop 2.x Cluster Architecture - Federation and High Availability, A Typical Production Hadoop Cluster, Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Single node cluster and Multi node cluster set up Hadoop Administration.

Learning Objectives :In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets. Topics-MapReduce Use Cases, Traditional way Vs MapReduce way, Why MapReduce, Hadoop 2.x MapReduce Architecture, Hadoop 2.x MapReduce Components, YARN MR Application Execution Flow, YARN Workflow, Anatomy of MapReduce Program, Demo on MapReduce. Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set.

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. Topics : Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format, Xml file Parsing using MapReduce.

Learning Objectives : In this module, you will learn Pig, types of use case we can use Pig, tight coupling between Pig and MapReduce, and Pig Latin scripting, PIG running modes, PIG UDF, Pig Streaming, Testing PIG Scripts. Demo on healthcare dataset.

Topics : About Pig, MapReduce Vs Pig, Pig Use Cases, Programming Structure in Pig, Pig Running Modes, Pig components, Pig Execution, Pig Latin Program, Data Models in Pig, Pig Data Types, Shell and Utility Commands, Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Specialized joins in Pig, Built In Functions ( Eval Function, Load and Store Functions, Math function, String Function, Date Function, Pig UDF, Piggybank, Parameter Substitution ( PIG macros and Pig Parameter substitution ), Pig Streaming, Testing Pig scripts with Punit, Aviation use case in PIG, Pig Demo on Healthcare Data set.

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.

Topics : Hive Background, Hive Use Case, About Hive, Hive Vs Pig, Hive Architecture and Components, Metastore in Hive, Limitations of Hive, Comparison with Traditional Database, Hive Data Types and Data Models, Partitions and Buckets, 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 Data set.

Learning Objectives : In this module, you will understand Advanced Hive concepts such as UDF, Dynamic Partitioning, Hive indexes and views, optimizations in hive. You will also acquire in-depth knowledge of HBase, HBase Architecture, running modes and its components.

Topics : Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive Indexes and views Hive query optimizers, Hive : Thrift Server, User Defined Functions, HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, Run Modes & Configuration, HBase Cluster Deployment.

Learning Objectives : This module will cover Advanced HBase concepts. We will see demos on Bulk Loading , Filters. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper.

Topics : HBase Data Model, HBase Shell, HBase Client API, Data Loading Techniques, ZooKeeper Data Model, Zookeeper Service, Zookeeper, Demos on Bulk Loading, Getting and Inserting Data, Filters in HBase.

Learning Objectives : In this module you will learn Spark ecosystem and its components, how scala is used in Spark, SparkContext. You will learn how to work in RDD in Spark. Demo will be there on running application on Spark Cluster, Comparing performance of MapReduce and Spark.

Topics : What is Apache Spark, Spark Ecosystem, Spark Components, History of Spark and Spark Versions/Releases, Spark a Polyglot, What is Scala?, Why Scala?, SparkContext, RDD.

Learning Objectives : In this module, you will understand working of multiple Hadoop ecosystem components together in a Hadoop implementation to solve Big Data problems. We will discuss multiple data sets and specifications of the project. This module will also cover Flume & Sqoop demo, Apache Oozie Workflow Scheduler for Hadoop Jobs, and Hadoop Talend integration.

Topics : Flume and Sqoop Demo, Oozie, Oozie Components, Oozie Workflow, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands, Oozie Web Console, Oozie for MapReduce, PIG, Hive, and Sqoop, Combine flow of MR, PIG, Hive in Oozie, Hadoop Project Demo, Hadoop Integration with Talend.