Azure SQL Devs’ Corner
Voices from the Azure SQL PM Team, focusing on development and developers
Latest posts
New T-SQL AI Features are now in Public Preview for Azure SQL and SQL database in Microsoft Fabric
At the start of this year, we released a new set of T-SQL AI features for embedding your relational data for AI applications. Today, we have brought those features to Azure SQL and SQL database in Microsoft Fabric. This post will help you get started using the new AI functions of Azure SQL. Prerequisites Set up your environment The following section guides you through setting up the environment and installing the necessary software and utilities. Set up the database The following section guides you through using the embeddings model to create vector arrays on relation data and use ...
MSSQL Extension for VS Code: GitHub Copilot Chat GA and New Public Previews for Edit Data, Data-tier Application, and more
The MSSQL Extension for VS Code continues to evolve, delivering features that make SQL development more integrated, more consistent, and more developer-friendly. In version v1.37.0, we’re announcing the Public Preview of Edit Data, Data-tier Application (DACPAC/BACPAC) export & import, and the SQL Database Projects Publish Dialog, along with the General Availability of GitHub Copilot — four capabilities that bring modern data editing, migration workflows, schema publishing, and AI-powered assistance directly into your development workflow inside Visual Studio Code. What’s new in MSSQL extension for VS Code...
Public preview of vector indexing in Azure SQL DB, Azure SQL MI, and SQL database in Microsoft Fabric
We are happy to share that DiskANN vector indexing is now in public preview across Azure SQL Database, Azure SQL Managed Instance, and SQL database in Microsoft Fabric. DiskANN is a cutting-edge algorithm for efficient vector similarity search, making it ideal for powering recommendation systems, image and multimedia search, and advanced retrieval-augmented generation (RAG) solutions. Built for large-scale vector search, DiskANN integrates seamlessly with the Microsoft SQL query engine, enabling you to combine semantic search with the full power of T‑SQL, joins, filters, analytics, without moving data or addin...
SQL Server 2025 Embraces Vectors: setting the foundation for empowering your data with AI
SQL Server 2025 marks a major milestone in database innovation by introducing native support for VECTOR data types and vector functions. These are now fully available in the freshly released SQL Server 2025 RTM. This means you can store and process high-dimensional embeddings directly within your database. It eliminates the need to move data across separate systems. With this integration, your data remains secure, synchronized, and governed by SQL Server’s enterprise-grade compliance and security features. The RTM release includes the VECTOR data type and built-in functions like VECTOR_DISTANCE. This suppor...
General Availability Announcement: Regex Support in SQL Server 2025 & Azure SQL
We’re excited to announce the General Availability (GA) of native Regex support in SQL Server 2025 and Azure SQL — a long-awaited capability that brings powerful pattern matching directly into T-SQL. This release marks a significant milestone in modernizing string operations and enabling advanced text processing scenarios natively within the database engine. What is Regex? The other day, while building LEGO with my 3-year-old — an activity that’s equal parts joy and chaos — I spent minutes digging for one tiny piece and thought, “If only Regex worked on LEGO.” That moment of playful frustration tur...
Build an AI Agentic RAG search application with React, SQL Azure and Azure Static Web Apps
Introduction Leveraging OpenAI for semantic searches on structured databases like Azure SQL enhances search accuracy and context-awareness, providing more relevant and insightful results. This example showcases the use of Azure SQL for storing and searching data with AI. By implementing the RAG pattern with Azure SQL, the sample efficiently searches for the most relevant code and comprehensive end-to-end examples based on user queries. The AI then generates results that include the most pertinent documents along with explanations of their relevance. The final result is a web application, mobile friendl...
Data API builder 1.6: Advanced Behaviors with Special HTTP Headers
Data API builder (DAB) provides REST and GraphQL endpoints over SQL Server, Azure Cosmos DB, PostgreSQL, MySQL, and SQL Data Warehouse. REST endpoints support several HTTP headers that let you control how requests behave. These headers give you precision over updates, caching, and discovering new resources. If-Match By default, DAB treats PUT and PATCH as upserts: update if the row exists, insert if not. Sometimes you need stricter semantics. If-Match provides update-only behavior. If-Match in DAB only supports *. Any other value is rejected. Example request that only updates if the row exists: ...
Data API builder 1.6: Flexible Logging for Every Developer
Previously, developers were limited to the default log levels and filters hardcoded into DAB. With release 1.6, you can now configure filters and levels for logs emitted by the engine. This release also adds new sinks. In addition to Application Insights and OpenTelemetry publishing, Data API builder now supports both file and Azure Log Analytics as targets. Rich, configurable logging wherever you need it. File sink (⭐ new!) Until now, DAB developers were mostly limited to console logs in the container. With release 1.6, you can sink logs to local files in a container folder to systematically debug and ob...
Using the new SqlVector type with EF Core and Dapper
Azure SQL vector support has been generally available for a few months now, and the ecosystem is quickly evolving to make working with vectors in your applications as seamless and efficient as possible. With the release of Microsoft.Data.SqlClient 6.1, developers can now take advantage of binary transport for vector data via the new class. This significantly improves performance when moving vectors between your application and the database, laying the groundwork for optimized vector handling in popular .NET libraries like: Here’s how you can start using in each of these libraries: EF Core 9...