Stop Guessing: How to Migrate Presto to BigQuery Without Breaking Your Analytics
Migrating your analytics from Presto to BigQuery is a strategic move — better scalability, serverless pricing, deeper integration with the Google Cloud ecosystem. But the migration itself is where teams lose weeks of engineering time and, worse, end up with reports their stakeholders can no longer trust.
Most Presto-to-BigQuery migrations don't fail on the big stuff. They fail on the small, invisible things: a function that flips its argument order, a type name that changes, an approximation function that's been renamed. The queries still parse without errors. They still return results. The results are just wrong — and nobody notices until a dashboard is questioned in a board meeting.
This post walks through the automated migration pipeline we use at Metadata Morph to move Presto query libraries to BigQuery safely and at scale — using SQLGlot for dialect translation, AST-based testing to validate structure, and DuckDB to prove the converted queries return identical results before anything touches your warehouse or your stakeholders.