Get ready to develop with SQL Server 2025

  I have not yet done looking at SQL Server 2022, SQL Server 2025 is ready to go. Microsoft is indeed relentless! 

Microsoft announced SQL Server 2025 at the Microsoft Ignite event in Chicago. Now , AI spans all over the Microsoft Eco system and that includes SQL Server. This time around Microsoft introduced SQL Server 2025 and SQL database on Fabric.

SQL Server is integrated with AI with:

• Vector search
• Vector Indexing using DiskANN
• T-SqL functions to support generation embedding and text chunking

Another useful feature added is the support for calling external REST APIs into SQL Server, a feature that existed in AZURE SQL Database. This AI feature makes it easy to store your AI models on premises and use them right away.

SQL Server 2025 will have optimized locking and will support native JSON datatype and improvements to Always on Availability groups. Also,GIT support for Microsoft Management Studio was announced. With this SSMS has now what is called a dark mode! 

Furthermore, a new product was announced, the SQL Database on Fabric. Microsoft Fabric is its unified analytics platform that integrates various datatools under one title. It is supposed to simplify data analysis and insights for both professionals and business users. 

Hey, Google does not have one such thing under a single title, but it has various functionalities dispersed in its cloud.

Let's move on!

More here:

https://www.microsoft.com/en-us/sql-server/blog/2024/11/19/announcing-microsoft-sql-server-2025-apply-for-the-preview-for-the-enterprise-ai-ready-database/

Vector Search

It is an incredibly useful tool. The reasons are listed here:

• It understands meaning, not just keywords: Unlike traditional search that relies on exact matches, vector search understands the semantic meaning and context of your query. This means you get more relevant results, even if you don't use the "right" words.   
• Handles diverse data types: Vector search isn't limited to text. It can work with images, audio, and even video, allowing you to search across different types of data.   
• Scales well: Vector search is designed to handle massive datasets, making it ideal for applications dealing with large amounts of information.   
• Powers advanced applications: Vector search is a key component in recommendation systems, anomaly detection, and semantic search, driving many of the online services we use daily.