Build Smarter Search Without Ai: Fuzzy Matching For Applications | Data Exposed

Not every search problem requires AI, embeddings, or vector databases.

Most business applications simply need to handle real-world data imperfections: misspelled customer names, inconsistent product descriptions, duplicate records, incomplete entries, and user input that rarely matches stored data exactly. Traditional string-matching techniques such as LIKE, CHARINDEX, PATINDEX, Full-Text Search, and even Regular Expressions solve many search scenarios, but they often struggle when the data itself is imperfect.

In this session, we'll explore the newest fuzzy string-matching capabilities in Azure SQL and SQL Server and see where they fit within the broader SQL search ecosystem. Through practical demonstrations, we'll compare fuzzy matching against existing approaches including LIKE predicates, Full-Text Search, and Regular Expressions, helping you understand the strengths, limitations, and ideal use cases for each technique.

Using real-world scenarios such as customer lookup, product search, duplicate detection, master data management, data cleansing, and application search experiences, you'll learn how to build typo-tolerant and user-friendly applications without introducing unnecessary AI complexity.

✅ Let's connect:
LinkedIn - Anna Hoffman, https://www.linkedin.com/in/amthomas46/
Twitter - AzureSQL, https://aka.ms/azuresqltw
LinkedIn - Iqra Shaikh, https://www.linkedin.com/in/shaikhiqra/

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