When the data volume is extra large, we may need to find a subset of data to speed up data analysis. This is sampling, a technique used to identify and analyze a subset of data in order to discover patterns and trends in the whole dataset. In HQL, there are three ways of sampling data: random sampling, bucket table sampling, and block sampling.
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Book Overview & Buying
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Table Of Contents
Apache Hive Essentials - Second Edition
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Apache Hive Essentials
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Overview of this book
In this book, we prepare you for your journey into big data by frstly introducing you to backgrounds in the big data domain, alongwith the process of setting up and getting familiar with your Hive working environment.
Next, the book guides you through discovering and transforming the values of big data with the help of examples. It also hones your skills in using the Hive language in an effcient manner. Toward the end, the book focuses on advanced topics, such as performance, security, and extensions in Hive, which will guide you on exciting adventures on this worthwhile big data journey.
By the end of the book, you will be familiar with Hive and able to work effeciently to find solutions to big data problems
Table of Contents (12 chapters)
Preface
Free Chapter
Overview of Big Data and Hive
Setting Up the Hive Environment
Data Definition and Description
Data Correlation and Scope
Data Manipulation
Data Aggregation and Sampling
Performance Considerations
Extensibility Considerations
Security Considerations
Working with Other Tools
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