Vector Data Architecture: Similarity Search and AI Retrieval
How embeddings, metadata, vector indexes, and retrieval services support semantic search and AI applications.
Articles page 2: more writing on software architecture, data, distributed systems, runtime choices, reliability, and engineering leadership.
How embeddings, metadata, vector indexes, and retrieval services support semantic search and AI applications.
How calculated metrics, aggregates, ML features, and materialized views are built, tested, served, and reused.
How catalogs, lineage, owners, schemas, quality signals, and freshness data make the data estate usable.
How country codes, currencies, tax categories, statuses, and policy lists are governed, versioned, searched, and analyzed.
How customer, product, supplier, employee, and location master data is matched, governed, published, and analyzed.
How relationship data supports fraud detection, recommendations, permissions, and connected search.
How coordinates, zones, routes, polygons, and distance queries become reliable operational and analytical systems.
How event streams power ETL, search projections, analytics, alerts, and operational decisions.
How metrics, sensors, prices, and operational measurements are stored, rolled up, searched, and analyzed.