Why learn Comprehensive MapReduce?
Today, when data is mushrooming and coming in heterogeneous forms, there is a growing need for a flexible, adaptable, efficient and cost effective data analytics which will take minimum on-boarding time. Hadoop fits just perfect in this space and MapReduce being the underlying engine for Hadoop needs to be well understood.
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.
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 in MapReduce, Combiner, Partitioner, Demos on MapReduce.
Advance MapReduce
Learning Objectives - In this module, you will learn Advance 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.