Learn about how we've improved vectoring indexing since we first released it, with Bob Ward and Anna Hoffman. ✅ Chapters 0:00 Context on vector indexing for Microsoft SQL 1:10 What's new 1:40 Iterative filtering 2:00 Demo starts 2:15 Named replicas in Azure SQL Database Hyperscale 2:30 Steps to create vector index 4:00 TOP WITH APPROXIMATE 5:00 What else you should know 5:32 How much faster it is 6:20 New certification DP 800 6:45 SQL AI in a day https://aka.ms/sqlaiad ✔️Resources: https://learn.microsoft.com/sql/sql-server/ai/vectors?view=sql-server-ver17 https://github.com/Azure-Samples/azure-sql-db-vector-search https://aka.ms/mssqldecks 📌 Let's connect: Twitter - Anna Hoffman, https://twitter.com/AnalyticAnna Twitter - AzureSQL, https://aka.ms/azuresqltw 🔴 Watch even more Data Exposed episodes: https://aka.ms/dataexposedyt 🔔 Subscribe to our channels for even more SQL tips: Microsoft Azure SQL: https://aka.ms/msazuresqlyt Microsoft SQL Server: https://aka.ms/mssqlserveryt Microsoft Developer: https://aka.ms/microsoftdeveloperyt #AzureSQL #SQL #LearnSQL

Google Vertex AI Vector Search introduces faster vector indexing, lower operational cost, and improved enterprise AI ROI for large-scale similarity search systems.