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diskann
About this tag
DiskANN is a disk-based approximate nearest neighbor search algorithm that enables high-performance vector similarity search directly within SQL Server and Azure PostgreSQL. Recent discussions highlight its integration into SQL Server 2025 RC0 for native vector search capabilities, allowing on-premises databases to support AI workloads without external services. In Azure PostgreSQL, DiskANN powers in-database AI features, enabling efficient vector indexing for semantic search and recommendation systems. The algorithm is designed for large-scale datasets, balancing speed and accuracy while running on standard hardware. These developments position DiskANN as a key technology for bringing vector search closer to relational data, reducing latency and operational complexity for AI applications.
Microsoft’s latest push turns PostgreSQL on Azure from a reliable cloud RDBMS into a full-fledged platform for AI-ready applications — and it’s doing so across three fronts: developer ergonomics, in-database AI capabilities, and a new, scale-out service aimed at AI-native workloads. The...
Microsoft Digital’s Employee Productivity Engineering (EPE) team faced a deceptively simple-sounding question with outsized implications: should we build on Microsoft Dataverse — the low-code data platform native to the Power Platform — or rely on Microsoft SQL Server and its mature relational...
ai integration
azure sql
backend architecture
cloud solutions
cost
data integration
dataverse
diskann
enterprise it
etl
governance
licensing
low-code development
performance
power platform
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sql server
vector indexing
Microsoft’s first Release Candidate (RC0) for SQL Server 2025 is here, and it’s more than a stability checkpoint—it’s a statement of direction that blends built-in AI, developer‑friendly T‑SQL, and secure‑by‑default networking into a single, on‑premises database platform that looks and feels...