Microsoft Developer presents an MVP Edition episode featuring Joseph D'Antoni, demonstrating how to implement vector search using SQL Server 2025 and Azure SQL for AI-powered applications.

Implementing Vector Search in Your Application with SQL Server 2025

Presented by Microsoft Developer, this Data Exposed: MVP Edition episode, featuring Joseph D'Antoni, dives into:

Key Topics

• Semantic Search: Moves beyond basic concepts, detailing how semantic search enables applications to understand context and meaning in data queries.
• Vector Search in SQL Server 2025: Explains vector storage and retrieval and shows implementation steps using Transact-SQL functions and SQL Server's new vector features.
• Data Ingestion at Scale: Techniques to ingest and manage large datasets for AI workloads, focusing on performance optimization within SQL Server and Azure SQL environments.
• Application Integration: Integrating vector search into real-world app scenarios to support modern AI needs.

Demo

• Step-by-step demo showing how to set up vector search on SQL Server 2025
• Implementing supporting features using T-SQL

Resources

• T-SQL Vector Search Functions Documentation
• SQL Server Vector AI Overview
• Azure SQL Vector Search Sample Code

About the Presenter

Joseph D'Antoni is a Principal Consultant and Microsoft Data Platform MVP, with over 20 years of experience in database platforms, performance tuning, infrastructure, and disaster recovery.

Community and Further Learning

• Connect on Twitter: Anna Hoffman, AzureSQL
• Check out other MVP Edition episodes and the broader Data Exposed series
• Subscribe for more SQL Server and Azure SQL tips: Microsoft Azure SQL, Microsoft SQL Server, Microsoft Developer

This episode equips developers and data professionals with practical knowledge to implement and optimize vector search and semantic search solutions for AI-powered apps using Microsoft data technologies.