In this chapter, we covered how to aggregate data using basic aggregation functions. Then, we introduced advanced aggregations with GROUPING SETS, ROLLUP, and CUBE, as well as aggregation conditions using HAVING. We also covered the various window functions. At the end of the chapter, we introduced three ways of sampling data. After going through this chapter, you should be able to do basic and advanced aggregations and data sampling in HQL. In the next chapter, we'll talk about performance considerations in Hive.
Apache Hive Essentials. - Second Edition
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Apache Hive Essentials. - Second Edition
By:
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
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Current Title:
Apache Hive Essentials. - Second Edition
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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