Greens Technologys Welcome’s you to best Hadoop Big Data Training Institute in Chennai
Greens Technologys is a leading Hadoop Big Data training Institute in Chennai which offer diverse range of training packages which cover wide range of Hadoop Big Data courses, you can be sure of our 100% commitment for courses. We have highly Hadoop Big Data experienced trainers who are working side by side on one of the biggest projects in IT Corporates and all over the world. Our experienced Hadoop Big Data trainers are regularly reviewing the curriculum of all Hadoop Big Data courses and keep it up to date according to industry trends. We also provide full flexibility to students regarding study timing. Students can choose their timing for their course of study with the mutual consent and availability of trainers.
Hadoop Big Data Job Prospects in India and abroad Due to the value added by Hadoop Big Data to medium and big sized businesses in terms of making them to become ever more efficient, the growth in the Hadoop Big Data is inevitable. Supporting many industries all over the world. Lot of organisations continue to implement Hadoop Big Data Solutions globally. Therefore there is a huge demand for Hadoop Big Data Professionals in India and all over the world to help enterprises implement Hadoop Big Data Solutions and to work on on-going support projects and also to work either on a self-employed basis or with an Hadoop Big Data training provider as a trainer training prospective Mobile Application Developers. This gives an opportunity to individuals with technical expertise in the field of Hadoop Big Data Solutions to fill this demand and supply gap in the workforce. Hadoop Big Data is also one of the smoothest ways of transition into the highly paid IT industry for people with non IT backgrounds. Opportunities in Hadoop Big Data are available in various industries and especially with companies in the technology sector such as IBM, Accenture, TCS, HP, Wipro Etc.
Hadoop Big Data Placements in Chennai with Greens Technologys, Although there is no guarantee of a job on course completion, we are almost certain that we shall be able to place you in a suitable position within a few weeks after successful completion of the course due to our position and reputation in the technology consulting industry and more importantly the network of organization’s we work 0with who use Hadoop Big Data for their enterprise needs.
Hadoop Big Data Training curriculum
1. Understanding Hadoop and Big Data
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 FileWriteandRead,RackAwareness.
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, Anatomy of File Write and Read, Rack Awareness.
2. Hadoop Architecture and HDFS
Learning Objectives – In this module, you will learn the Hadoop Cluster Architecture, Important Configuration files in a Hadoop Cluster, Data Loading Techniques.
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, Password-Less SSH, MapReduce Job Execution, Data Loading Techniques: Hadoop Copy Commands, FLUME, SQOOP.
3. Hadoop MapReduce Framework – I
Learning Objectives – In this module, you will understand Hadoop MapReduce framework and the working of MapReduce on data stored in HDFS. You will learn about YARN concepts in MapReduce.
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.
4. Hadoop MapReduce Framework – II
Learning Objectives – In this module, you will understand concepts like Input Splits in MapReduce, Combiner & Partitioner and Demos on MapReduce using different data sets.
Topics – Input Splits, Relation between Input Splits and HDFS Blocks, MapReduce Job Submission Flow, Demo of Input Splits, MapReduce: Combiner & Partitioner, Demo on de-identifying Health Care Data set, Demo on Weather Data set.
5. Advanced MapReduce
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 how to deal with complex MapReduce programs.
Topics – Counters, Distributed Cache, MRunit, Reduce Join, Custom Input Format, Sequence Input Format.
6. Pig
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.
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.
Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF, Pig Demo on Healthcare Data set.
7. Hive
Learning Objectives – This module will help you in understanding Hive concepts, Loading and Querying Data in Hive 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, Hive Demo on Healthcare Data set.
8. Advanced Hive and HBase
Learning Objectives – In this module, you will understand Advanced Hive concepts such as UDF, Dynamic Partitioning. You will also acquire in-depth knowledge of HBase, HBase Architecture and its components.
Topics – Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts, Hive : Thrift Server, User Defined Functions.
HBase: Introduction to NoSQL Databases and HBase, HBase v/s RDBMS, HBase Components, HBase Architecture, HBase Cluster Deployment.
9. Advanced HBase
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.
10. Oozie and Hadoop Project
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 and Apache Oozie Workflow Scheduler for Hadoop Jobs.
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, Hadoop Project Demo.
