Vector databases, lakehouses, streaming, and feature platforms.
Data EngineeringFeature stores bridge data engineering and production ML by centralizing feature definitions, versioning, and low-latency serving. This guide compares 2026 architectures and platforms while addressing training-serving skew, governance, and adoption tradeoffs.
Data EngineeringBatch processing won the last decade. It was predictable, correct, and easy to reason about. But AI in 2026 does not wait for the next ETL run. Fraud detection, real-time personalization, predictive maintenance, and agentic systems all require...
Data EngineeringRetrieval-augmented generation (RAG) has become the default architecture for production AI in 2026. You cannot fit an enterprise knowledge base into even the largest context windows, so you embed your documents, store the vectors, and retrieve the...