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Using DBQL Object Data for Targeting MLPPI and Columnar Candidacy

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Have you ever struggled over whether, or how, to use a new database feature?  In this presentation, we’ll demonstrate how database query log (DBQL) data can help when it comes to determining candidates for MLPPI (Multi-Level Partitioned Primary Index) as well as the new Columnar Partitioning feature of Teradata 14.

This presentation will discuss using DBQL object data for gathering such key information as frequency of use, large scan, and total query counts, as well as CPU and I/O usage.  In addition, the presentation will review techniques for testing and validating conclusions, instilling confidence in your decisions.  Finally, learn how the wealth of information available from DBQL can make it easier for you to benefit from the performance advantages that come with features like MLPPI and Columnar.

Note: This was a 2012 Teradata Partners Conference session.

Teradata Release information: TD 13 & 14

Presenters:
Barbara Christjohn, Performance COE – Teradata Corporation
David Hensel, Performance COE – Teradata Corporation

Audience: 
Data Warehouse Administrator, Data Warehouse Architect/Designer, Data Warehouse Technical Specialist
Training details
Course Number: 
50495
Training Format: 
Recorded webcast
Price: 
$195
Credit Hours: 
1
Channel: 

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