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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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Author (1):
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Dayong Du Dayong Du
Author Profile Icon Dayong Du
Dayong Du
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Table of Contents (12) Chapters Close

Preface 1. Overview of Big Data and Hive 2. Setting Up the Hive Environment FREE CHAPTER 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

Data exchange with INSERT

To extract data from tables/partitions, we can use the INSERT keyword. Like other relational databases, Hive supports inserting data into a table by selecting data from another table. This is a very common ETL (a term in data warehousing for Extract, Transform, and Load) pattern used to populate an existing or new table from another table or dataset. The HQL INSERT statement has the same syntax as a relational database's INSERT. However, HQL has improved its INSERT statement by supporting data overwrittening, multi-insert, dynamic partition insert, as well as inserting data into files. The following are a few examples of INSERT statements in HQL:

  1. The following is a regular INSERT from the SELECT statement:
      -- Check the target table, which is empty.
> SELECT name, work_place FROM employee;
+-------------+-------------------+
...
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