Vector search is rapidly becoming a cornerstone of modern data systems, enabling semantic retrieval that goes beyond traditional keyword matching. With SQL Server 2025, this capability is now natively integrated, allowing organizations to store and query embeddings alongside structured data for intelligent, context-aware search.
In this session, we’ll break down:
• What is Vector Search?
A clear explanation of embeddings, similarity measures, and why they matter in today’s AI-driven landscape.
• How SQL Server 2025 Implements It:
Explore the new vector data types, indexing strategies, and query syntax that make semantic search possible within a familiar relational environment.
• Design Patterns and Best Practices:
Learn how to combine vector search with traditional SQL filters for hybrid retrieval, optimize performance, and maintain governance.
• Business Use Cases (Quick Overview):
From semantic product search and duplicate detection to customer support knowledge retrieval and personalization, see where vector search delivers tangible ROI.
By the end of this talk, you’ll have a solid understanding of vector search fundamentals, how SQL Server 2025 supports them, and where to apply these capabilities for maximum business impact.
Key Takeaways:

Core concepts behind vector search and embeddings.
Native SQL Server 2025 features for vector storage and querying.
Practical scenarios where semantic search outperforms keyword-based approaches.