Why Your Data Platform Runs Two Engines — And Why That's Costing You
· 11 min read
Take an honest look at your data platform architecture. If you are running a lakehouse on AWS, there is a good chance it looks something like this: Spark clusters for ETL and data engineering, plus Trino (or Dremio, or Presto) clusters for analytics and BI queries. Two engines, two teams, two bills — all pointed at the same data.
This dual-runtime pattern has become the default architecture for most modern data platforms. And on the surface, it makes sense. Spark is great at processing data. Trino is great at querying it. Each engine solves a real problem.
But running two engines has hidden costs that most organizations never quantify — and once you add them up, the number is hard to ignore.