Why your warehouse is slow (it's probably not the warehouse)
Nine times out of ten, warehouse performance issues are model-design issues. Here's how to tell.
When a warehouse is slow, the first instinct is to blame the vendor and scale up compute. The second instinct — usually correct — is to look at the models.
The pathologies we see most often: fact tables joined to slowly-changing dimensions without appropriate keys; late-arriving data forcing full-table rebuilds; incremental logic that reprocesses more than it needs to; and semantic layer queries that fan out to dozens of underlying joins.
Fixing these is usually a week of focused work by someone who understands the model. Scaling compute to mask them costs six figures a year, forever.
Priya leads Dataamps' data platform practice, with 15 years shipping lakehouse and streaming systems inside Fortune 500 enterprises.
More from PriyaRelated articles
Data contracts are boring — and that's the point
Why the least glamorous idea in the modern data stack is quietly the highest-leverage one for teams tired of 3AM pages.
Notes from an Iceberg migration
Lessons from moving 12 PB of data to Apache Iceberg without downtime — and what we'd do differently.
How we evaluate LLM applications in production
A practical evaluation framework for teams shipping copilots and RAG systems — without inventing a research lab.