Experienced Trainer ,given lot of batches.

Topics Covered

Hadoop Developer Training Introduction Elegant IT Services, a Bay Area startup, provides professional Big Data services and facilitates the extraction of nuggets of information to support data-based decision making. We are Hadoop specialists and our specially trained professionals help our client’s tame Big Data challenges. Course Objective Summary During this course, you will learn: • Introduction to Big Data and Hadoop • Hadoop ecosystem - Concepts • Hadoop Map-reduce concepts and features • Developing the map-reduce Applications • Pig concepts • Hive concepts • HBASE Concepts • Mongo DB Concepts • Sqoop Concepts • Real Life Use Cases Introduction to Big Data and Hadoop • What is Big Data? • What are the challenges for processing big data? • What technologies support big data? • What is Hadoop? • Why Hadoop? • History of Hadoop • Use Cases of Hadoop • Hadoop eco System • HDFS • Map Reduce • Statistics Understanding the Cluster •?Typical workflow • Writing files to HDFS • Reading files from HDFS • Rack Awareness • 5 daemons Let's talk Map Reduce • Before Map reduce • Map Reduce Overview • Word Count Problem • Word Count Flow and Solution • Map Reduce Flow • Algorithms for simple problems • Algorithms for complex problems Developing the Map Reduce Application • Data Types • File Formats • Explain the Driver, Mapper and Reducer code • Configuring development environment - Eclipse • Writing Unit Test • Running locally • Running on Cluster • Hands on exercises c • Anatomy of Map Reduce Job run • Job Submission • Job Initialization • Task Assignment • Job Completion • Job Scheduling • Job Failures • Shuffle and sort • Oozie Workflows • Hands on Exercises Map Reduce Types and Formats • MapReduce Types • Input Formats - Input splits & records, text input,binary input, multiple inputs & database input. • Output Formats - text Output, binary output, multiple outputs, lazy output and database output • Hands on Exercises. Map Reduce Features •?Counters • Sorting • Joins - Map Side and Reduce Side • Side Data Distribution • MapReduce Combiner • MapReduce Partitioner • MapReduce Distributed Cache • Hands Exercises Hive and PIG • Fundamentals • When to Use PIG and HIVE • Concepts • Hands on Exercises HBASE • CAP Theorem • Hbase Architecture and concepts • Programming and Hands on Exercises Case Studies Discussions

Who should attend

All IT Guys

Pre-requisites

Basic Java

What you need to bring

Nothing

Key Takeaways

Handson ,Gud Knowledge