Data & Databases
Modeling, querying, and scaling data, from relational design to vector search and Azure Cosmos DB.
- Vector Databases in AI/ML: the next-gen infrastructure for intelligent search — Vector databases are transforming how AI retrieves and understands information. Whether it's LLMs using RAG, AI-powered search, or real-time personalization, they're becoming essential for intelligent systems.
- Code-First vs Database-First - which camp are you in? — There are two common approaches to friendship between your application and database. Let's review.
- Azure Cosmos DB: Introduction — Curious about what makes Cosmos DB a go-to solution for globally distributed NoSQL workloads? Here is a first introduction to Azure Cosmos DB.
- Azure Cosmos DB Core (SQL) API — A deep-dive into Azure Cosmos DB's Core (SQL) API - the native NoSQL engine that gives you the power of JSON + SQL with automatic scaling, rich queries, and global availability out of the box.
- Building a RAG System with Local Vector Search — A hands-on guide to RAG on your own hardware: chunking, open embeddings, a local vector store, retrieval, reranking, and wiring it to a model.
- Visualizing Embeddings at Scale with Apple's Embedding Atlas — Apple's Embedding Atlas renders millions of embeddings interactively in a browser tab by shipping DuckDB-WASM, Mosaic, and a WebGPU pipeline to the client. A look at the data-engineering choices that keep exploration honest at scale, plus how to point the CLI at your own Parquet file.
- The Hidden Limit of RAG: Why Vector Dimensions Cap Your Dataset — Retrieval quality isn't only a model problem. A single embedding vector's dimension sets a hard, geometric ceiling on how many document combinations it can ever return. Here's the math, the benchmark that exposes it, and what to build instead.
- Semantic Code Search: How Embeddings Speed Up AI Coding Assistants — Grep finds strings; AI agents need to find meaning. A look at how Cursor trained a custom embedding model on agent traces, the measured accuracy gains, and the vector-database plumbing that makes semantic code search work at scale.
- Editing SQL Data with AI: Copilot in the VS Code MSSQL Extension — The MSSQL extension for VS Code v1.37 shipped two ways to change table data without hand-writing DML: the Edit Data grid and Copilot agent mode. They look alike but have very different trust models. Here's how to use each safely.
- Copilot in SSMS: AI-Assisted T-SQL in SQL Server Management Studio — GitHub Copilot in SSMS 22 isn't fancier autocomplete. It runs inside your session, under your login, aware of your connection, schema, and execution plans. A practical look at the context model, slash commands, agent mode, and where the permission boundary makes AI-assisted T-SQL actually safe.
- Building Cosmos DB Infrastructure with an AI Agent Kit — An AI coding agent will happily write Cosmos DB Terraform that plans cleanly and quietly bakes in the wrong defaults. Microsoft's open-source Cosmos DB Agent Kit is a knowledge layer: 120+ battle-tested rules. That flips those defaults toward production. Here's what it changes in a real infra revi
- Postgres for the AI Era: Inside Azure HorizonDB — Azure HorizonDB rebuilds Postgres on a database-as-logs engine and pushes vector search and model inference inside the transaction boundary. A look at the architecture, DiskANN filtered search, and what adopting it actually costs.