Big data refers to the use of advanced data analytics methods that help extract value from large data sets both structured an...
Mode Of Delivery
Module 1 - Introduction to Big Data
Rise of Big Data, Hadoop vs traditional systems, Hadoop Master-Slave architecture, HDFS Architecture, NameNode, DataNode, Secondary Node, JobTracker, TaskTracker.
Module 2 – HDFS and MapReduce architecture
Core components of Hadoop, Anatomy of Read and Write data on HDFS, MapReduce architecture Flow, JobTracker and TaskTracker.
Module 3 - Hadoop Configuration
Hadoop modes, Hadoop terminal commands, Cluster configuration, Web ports, Hadoop configuration files, Reporting, Recovery, MapReduce in action.
Module 4 - Understanding Hadoop MapReduce framework
Overview of the MapReduce framework, Use cases of MapReduce, Anatomy of MapReduce program, Mapper/Reducer class, Driver code and Combiner and Partitioner.
Module 5: Advance MapReduce - Part 1
Write your own Partitioner, Writing Map and Reduce, Map side/Reduce side Join, Distributed Join, Distributed cache, Counters and joining Multiple datasets in MapReduce.
Module 6: Advance MapReduce - Part 2
MapReduce internals, Input format, Custom input format, Writable and Comparable, Output format, files, JUnit and MRUnit testing frameworks.
Module 7: Apache Pig
PIG vs MapReduce, PIG architecture & Data types, PIG Latin relational operators, PIG Latin Join and CoGroup, PIG Latin group and union, Describe, Explain, Illustrate, PIG Latin: File loaders & UDF.
Who Should Attend?
After completing this course and successfully passing the certification examination, the student will be awarded the “Big Data Analytics with Hadoop1” certification.
If a learner chooses not to take up the examination, they will still get a 'Participation Certificate'