SQL Server 2025 Adds Native Regex: What It Means for AnalyticsCreator Users

SQL Server 2025 introduces REGEXP_LIKE, REGEXP_REPLACE, and REGEXP_SUBSTR to T-SQL. These work out-of-the-box with AnalyticsCreator, enabling regex-based cleansing, validation, and parsing in your transformations—no manual integration required.

What’s New in SQL Server 2025

• REGEXP_LIKE: Tests if a pattern matches (boolean)
• REGEXP_REPLACE: Replaces pattern matches in strings
• REGEXP_SUBSTR: Extracts matched substrings

Supported flags include: i (ignore case), m (multi-line), s (dot matches newline), and c (case-sensitive by default).

Why This Matters for AnalyticsCreator Users

Regex is now natively available in SQL Server 2025 and Azure SQL. AnalyticsCreator users can define regex logic in calculated columns or transformation steps—AnalyticsCreator compiles it to native T-SQL, no special configuration required.

Common Use Cases

• Cleanse product numbers or codes
• Validate email or pattern formats
• Extract values from unstructured strings

Working Example (AdventureWorksLT)

REGEXP_REPLACE([T1].[ProductNumber], '-', '')

This removes dashes from product numbers in a transformation view. Simply enter this in the Statement field of a calculated column in AnalyticsCreator.

Additional Regex Test Scenarios

• Email validation
• Extracting numeric tokens
• Normalizing whitespace
• Case-sensitive vs case-insensitive pattern matching
• Targeted replacements by match occurrence
• Capture group extraction

Environment Requirements

• SQL Server 2025 (preview) or Azure SQL with regex support
• Patterns follow PCRE-style syntax

Performance Tips

• Use regex for complex patterns, not simple LIKE alternatives
• Persist results for large datasets
• Index cleaned columns used in joins or filters

Suggested AnalyticsCreator Test Plan

1. Test regex on SQL Server 2019, 2022, and 2025
2. Create transformation steps using each REGEXP function
3. Validate data integrity and output
4. Benchmark regex performance at scale
5. Combine with historization patterns for robust pipelines

Conclusion

Native regex support in SQL Server 2025 empowers AnalyticsCreator users to enrich data quality, enforce patterns, and simplify cleansing—directly in the transformation layer. No external tools or scripts needed. Just metadata-driven modeling, compiled for performance.