Showing category results for T-SQL

At a glance — Native regular expression (regex) functions in T-SQL now accept and inputs of up to 2 MB across all seven regex functions, including the two table-valued functions ( and ). This capability ships in SQL Server 2025 CU5 and is already available in Azure SQL Database, SQL Database in Fabric and Azure SQL Managed Instance configured wit...

Artificial Intelligence is evolving rapidly. What began as simple prompt-and-response systems is now transforming into fully autonomous, agentic AI architectures capable of reasoning, orchestrating tools, interacting with enterprise data, and invoking external systems dynamically. While these capabilities unlock enormous business potential, they al...

The Agent-Ready Database: Security, Backup, and MCP Part 4 of 4 – The Multi-Model Database Series This is the final post in a four-part series on multi-model databases in SQL Server 2025 and Azure SQL - exploring how the optimizer, storage engine, and security layer treat each data model as a first-class citizen under one roof. In Part 1: The ...

Remember when we announced the Public Preview of DiskANN vector indexes back in November and mentioned that once you created the index, your table became read‑only? Yeah… about that... 😅 We shipped early because the demand for Vector search in SQL was overwhelming. We knew the constraints weren’t ideal, but we also knew the fastest way to get this ...

When JSON Met Graph Part 2 of 4 - The Multi-model Database Series A note on naming. Throughout this series, when we say "SQL Server 2025" we also mean Azure SQL. The multi-model capabilities we discuss - native JSON, graph, vector, and columnstore - are available across both the on-premises engine and the Azure SQL family. In Part 1 we des...

Part 1 of 4 - The Multi-model Database Series This is a four-part series about what happens when a single database engine handles relational, document, graph, vector, and analytical workloads natively - and what you stop paying for when it does. You spin up a database, point an agent at it, and start building. The first few tables go fast - use...

Applications often need to know what data looked like before. Who changed it, when it changed, and what the previous values were. Rebuilding that history in application code is tedious and error prone. This is especially valuable when exposing a database to an AI agent through MCP servers like SQL MCP Server, where information discovery matters. ...

Applications often need access to data without needing access to everything. Social Security numbers, email addresses, and phone numbers are common examples. Storing them is required. Exposing them broadly is not. This is especially valuable when exposing a database to an AI agent through MCP servers like SQL MCP Server, where safety and reversibil...

Sometimes applications need to remove data without actually losing it. Soft delete keeps rows in the database while making them invisible to normal application access. This is especially valuable when exposing a database to an AI agent through MCP servers like SQL MCP Server, where safety and reversibility matter. Learn more about SQL MCP Serve...

Supporting re‑ranking has been one of the most common requests lately. While not always essential, it can be a valuable addition to a solution when you want to improve the precision of your results. Unfortunately, there isn’t a universal, standardized API for a “re‑rank” call across providers, so the most reliable approach today is to issue a manua...