Learn how Teradata applies two effective join optimization methods, Early and Partial GROUP BY, for decision support workloads with lots of aggregations. These transformations reduce resource usage and are widely used in modern databases. Discover how to improve the optimizer’s chance to apply these techniques by collecting statistics on all join and aggregation columns.
Learn about Teradata value ordering, an optimization technique for range scans of 32-bit integers. Read on to understand how it is implemented.
Learn about the characteristics of static and dynamic SQL in Teradata stored procedures. Discover how to use variables and parameters in SQL statements.
Learn about Teradata Partitioning and how row and column partitioning can minimize data access resource usage. Read on for benefits and examples.
Learn how to avoid resource-killing full table scans on extensive tables with Teradata Indexing. Discover a fantastic trick to optimize query performance.
Discover the improvements for Teradata statistics with the new syntax starting from Release 14.00. Learn how to optimize performance and usage.
Teradata SQL Stored Procedures enhance traditional SQL with procedural language features like iterations, condition and error handling, and variables. Learn more here.
Learn about Teradata’s MaxValueLength feature, which allows users to specify how many bytes or characters should be used when creating statistics histograms.
Learn about Teradata’s load utilities, which can be divided into two groups: those that bypass the transient journal and those that use it. Depending on your requirements, you may choose to load data either way. This article offers advice on how and when to use each tool, including BTEQ, TPump, Fastload, Multiload, and the Teradata Parallel Transporter (TPT). Find out which tool is most efficient for your data quantity and row size.
Learn how to solve the problem of accessing a table containing many rows using Teradata Partition Elimination with a Stored Procedure.
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