Writing from inside real data systems

A practical guide to a modern lakehouse on open table formats — without dragging a Spark cluster into the picture.

Chunking strategies, vector store trade-offs, and the evaluation loop that keeps retrieval honest in production.

An end-to-end architecture for ingest, storage, transform and serve that stays scalable, secure and cost-aware.

Late-arriving data, backfills and idempotency — the three problems every incremental model eventually meets.

Why model choice, dimensionality and refresh cadence matter more to your bill than the vector database you pick.

Single-node analytics has quietly become fast enough for most teams. Here is how to tell if yours is one of them.