Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns and other useful information that can provide competitive advantages and better business results.
Big data analytics can be done with the software tools commonly used - but the unstructured data sources used for big data analytics may not fit in traditional data warehouses. Additionally, traditional data warehouses may not be able to handle the processing demands posed by big data. As a result, a new class of big data technology has emerged and is being used in many big data analytics environments.
In this course participants will learn how to analyze Big Data stored in Hadoop by using Pig and Hive.
This course is designed to introduce you to Hadoop architecture and MapReduce framework, Pig scripting language and Hive
Understand Hadoop architecture and its main components
Data ETL with Hadoop tools
Write Pig scripts
Use Hive's SQL dialect to query and analyze large datasets store in Hadoop
Module 1: Introduction to Big Data
Module 2: Introduction to Hadoop
Module 3: Pig
Module 4: Pig Advanced
Module 5: Hive
Module 6: Hive Data Types
Module 7: HiveQL - DDL
Module 8: HiveQL – Data Manipulation
Module 9: HiveQL - Queries
Module 10: HiveQL - Views
Module 11: HiveQL - Indexes
Module 12: Schema design
Module 13: Hive advanced
Module 14: Functions
Module 15: Advance format types