Every "BigQuery vs Snowflake cost" article I found while researching this was at least a year old, and cloud pricing changes often enough that the numbers were already wrong.
I wanted something that stayed correct without me touching it, so I built bigdataexplained.com: a static site (Astro, deployed on Cloudflare Pages) backed by a GitHub Actions job that runs daily, pulls current rates from each vendor's pricing API where one exists, and falls back to the published rate card where it doesn't.
A few things I had to solve:
Detecting real price changes vs noise. The daily job diffs the incoming dataset against the last one, ignoring timestamp fields, so a commit only says "price changed" when a rate actually moved.
Not silently going stale. If a vendor API starts failing, the site keeps the last confirmed value but flags it as "refresh failing" rather than pretending it's current.
Making it useful, not just accurate. The site has a calculator for your own workload shape, and head-to-head pages comparing any two engines across data volumes and workload profiles (BI dashboard, ETL, ad hoc analytics).
It's fully static, no backend, no database beyond a committed JSON file that IS the database.
Live at https://bigdataexplained.com — happy to answer questions about the pipeline or the pricing model assumptions.
