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Apache Hive Essentials

You're reading from   Apache Hive Essentials Essential techniques to help you process, and get unique insights from, big data

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788995092
Length 210 pages
Edition 2nd Edition
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Concepts
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Author (1):
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Dayong Du Dayong Du
Author Profile Icon Dayong Du
Dayong Du
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Toc

Table of Contents (12) Chapters Close

Preface 1. Overview of Big Data and Hive FREE CHAPTER 2. Setting Up the Hive Environment 3. Data Definition and Description 4. Data Correlation and Scope 5. Data Manipulation 6. Data Aggregation and Sampling 7. Performance Considerations 8. Extensibility Considerations 9. Security Considerations 10. Working with Other Tools 11. Other Books You May Enjoy

Views

Views are logical data structures that can be used to simplify queries by hiding the complexities, such as joins, subqueries, and filters. It is called logical because views are only defined in metastore without the footprint in HDFS. Unlike what's in the relational database, views in HQL do not store data or get materialized. Once the view is created, its schema is frozen immediately. Subsequent changes to the underlying tables (for example, adding a column) will not be reflected in the view's schema. If an underlying table is dropped or changed, subsequent attempts to query the invalid view will fail. In addition, views are read-only and may not be used as the target of the LOAD/INSERT/ALTER statements.

The following is an example of a view creation statement:

> CREATE VIEW IF NOT EXISTS employee_skills
> AS
> SELECT 
> name, skills_score['DB...
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