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Another Look at Tuning Big Data Queries

Most large organizations have implemented one or more big data applications. As more data accumulates internal users and analysts execute more reports and forecasts, which leads to additional queries and analysis, and more reporting. The cycle continues: data growth leads to better analysis, which generates more reporting. Eventually the big data application swells with so much data and querying that performance suffers.

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The DRI Subject of References

A database must be able to maintain and enforce the business rules and relationships in data in order to maintain the data model. It does this through referential constraints. They aren't complex, but are powerful, especially with the means to attach DRI actions to them. Joe Celko explains all, and pines for the ANSI CREATE ASSERTION statement.

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The Importance of Caching

Performance tuning and optimization definitely have their place in minimizing SQL Server Licensing costs – by helping keep CPU utilization low. But it’s important to remember that the fastest and most efficient query possible is the one that you never execute against your SQL Server. That might sound trite, but it’s at the heart of caching – which is key to helping organizations save significant money on SQL Server licensing costs while simultaneously enabling better application performance and increased scalability.

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Question of the Day

Missing the Jaro Winkler Distance

I upgraded a SQL Server 2019 instance to SQL Server 2025. I wanted to test the fuzzy string search functions. I run this code:

SELECT JARO_WINKLER_DISTANCE('tim', 'tom')
I get this error message:
Msg 195, Level 15, State 10, Line 1 'JARO_WINKLER_DISTANCE' is not a recognized built-in function name.
What is wrong?

See possible answers