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Teradata Cookbook

You're reading from   Teradata Cookbook Over 85 recipes to implement efficient data warehousing solutions

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787280786
Length 454 pages
Edition 1st Edition
Languages
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Authors (3):
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 Khandelwal Khandelwal
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Khandelwal
 Kasi Kasi
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Kasi
 Bhamidipati Bhamidipati
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Bhamidipati
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Toc

Table of Contents (19) Chapters Close

Title Page
Dedication
Packt Upsell
Contributors
Preface
1. Installation FREE CHAPTER 2. SQLs 3. Advanced SQL with Backup and Restore 4. All about Indexes 5. Mixing Strategies – Joining of Tables 6. Building Loading Utility – Replication and Loading 7. Monitoring the better way 8. Collect Statistics the Better Way 9. Application and OPS DBA Insight 10. DBA Insight 11. Performance Tuning 12. Troubleshooting 1. Other Books You May Enjoy Index

Archiving PPI tables


Teradata provides various indexing mechanisms, and Partitioned Primary Index (PPI) is one of them. PPU tables are useful in improving the performance of queries that utilize range-based data. It helps to restrict data retrieval to the partitions in which rows are present. In normal cases, rows are inserted into a table, they are stored in an AMP, and arranged by their row hash order. With the table defined with PPI, the rows are sorted by their partition number and are stored in a collective manner in their partitions. Within each partition, they are arranged by their row hash. Rows are assigned to a partition based on the partition expression defined.

Teradata provides two types of partitions on tables as show in the following figure:

  • Row level partition
  • Column level partition

You can perform an all-AMPs archive on one or more partitions of a table rather than performing a full-table backup. The ability to select partitions from PPI tables is limited to all-AMP archives...

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