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What's new in SQL Server 2025 and Azure SQL Databases - Christian Peeters - NDC Oslo 2026

Published 2026-10-28 · Duration 1:00:27

Summary

SQL Server 2025 and Azure SQL Databases are evolving beyond simple storage engines to become active components in AI-driven architectures. Key innovations include native support for vector data types, enabling semantic search and Retrieval-Augmented Generation (RAG) directly within the database. Furthermore, developer productivity is significantly boosted by enhanced T-SQL capabilities, such as calling external REST endpoints, advanced JSON indexing, and robust pattern matching using Regular Expressions (RegEx).

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Key takeaways

Native Vector Database Capabilities 17:00
The introduction of the `vector` data type allows SQL Server to store embeddings (floating-point representations of meaning) for advanced AI workloads. This enables semantic search and RAG by calculating similarity (e.g., using cosine distance) directly within SQL, drastically simplifying complex data pipelines.
Enhanced Developer Productivity via T-SQL 30:00
Developers can now perform complex operations directly in SQL, including calling external REST endpoints (HTTP calls) to interact with external services, improving system integration without leaving the database layer.
Advanced Data Handling and Pattern Matching 26:40
Improvements include native `JSON` data type support (allowing indexing on semi-structured data properties), enhanced Regular Expressions (RegEx) for data sanitization and masking, and Fuzzy Search algorithms (like Jaro-Winkler) to handle user spelling errors.
Real-Time Event Streaming 46:40
The addition of Change Event Streaming allows every `INSERT`, `UPDATE`, or `DELETE` operation to stream an event (including the old and new object versions) to external services like Azure Event Hubs, facilitating real-time auditing and event-driven architectures.

Technical details

Vector Search and RAG 1020s
SQL Server 2025 introduces the `vector` data type and functions like `vector distance` and `vector search`. This enables RAG by storing embeddings and calculating similarity metrics (cosine, Euclidean, dot product) to find semantically related data, moving beyond keyword matching. Indexing is available for performance optimization.
JSON Data Type and Indexing 1600s
The native `JSON` data type allows storing semi-structured data as binary objects. Crucially, it enables creating indexes on specific properties within the JSON structure (e.g., using `JSON_CONTAINS`), providing massive performance gains over previous methods.
T-SQL REST API Integration 1800s
SQL Server can now execute HTTP calls and REST API calls to external endpoints. This allows the database to interact with external services, such as fetching data or performing actions, directly within T-SQL statements.
Pattern Matching and Data Quality 1400s
New capabilities include advanced Regular Expressions (RegEx) for data extraction and sanitization (e.g., masking emails or extracting model numbers). Fuzzy Search algorithms (e.g., Jaro-Winkler) address data quality issues caused by user spelling errors.

Mentioned resources

NDC Conferences (Conference)
Azure AI Foundry (Model Endpoint)

Channel & topics

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