2026-02-13
849 reads
2026-02-13
849 reads
In the last article, we examined fuzzy string matching in SQL Server 2025 with a few new functions. We know comparing strings has always been hard when we don't have great data quality. If we need exact matches, SQL Server works great. However, we often expect users to enter values without typos and know what […]
2026-01-23
1,803 reads
Comparing strings has always been hard when we don't have great data quality. If we need exact matches, SQL Server works great. However, we often expect users to enter values without typos and know what values they want to find. Or at least know part of the string. However, matching with wildcards or partial strings […]
2026-01-28 (first published: 2026-01-16)
2,607 reads
Learn about the new string similarity functions in Azure SQL Database.
2025-03-28
6,826 reads
An optimized Damerau-Levenshtein Distance (DLD) algorithm for "fuzzy" string matching in Transact-SQL 2000-2008
2014-01-10 (first published: 2012-09-18)
31,112 reads
Roll Your Own Fuzzy Match / Grouping (Jaro Winkler) - T-SQL
2009-06-10
44,871 reads
By Steve Jones
I haven’t done one of these in awhile, but I saw an article recently...
In last months one of the scenarios where you can use AI has been...
By ChrisJenkins
Do you spend so long manipulating your data into something vaguely useful that you...
Comments posted to this topic are about the item Creating JSON II
Comments posted to this topic are about the item Engineer Lessons
On SQL Server 2025, what happens when I run this code:
SELECT JSON_OBJECTAGG( N'City':N'Denver' RETURNING JSON) GOSee possible answers