Redshift to Snowflake at AI-accelerated pace
Purpose-built skills to translate the code and move the workloads, so the migration was limited by review capacity rather than by typing speed.
The problem
Warehouse migrations stall on two things. First the sheer volume of dialect translation, which is tedious rather than hard and quietly consumes most of the calendar. Second the trust question at the end, which is whether anyone can prove the numbers still tie. The work is too big to do by hand and too consequential to do carelessly.
What we built
We moved the warehouse from Redshift to Snowflake using skills built for the job. One set translated SQL between the two dialects and built out the models. Another moved the workloads themselves across, so scheduled jobs and transformations landed in Snowflake as running workloads rather than being rewritten by hand a second time. Generated output was reviewed and tested rather than trusted on sight, which is what makes the speed usable instead of merely fast.
The impact
The migration compressed substantially against a manual timeline, and the throughput came from tooling rather than from adding people to the project. The team came out of it on a warehouse better matched to how they actually query, instead of carrying an aging environment forward because moving off it looked too expensive.
What they own now
The Snowflake models and workloads, and the translation tooling used to get there.
We publish client work without naming the client. No logos, no business metrics, no data. If you want references, we make them on a call, with the client's blessing.
Same build, your data. Tell us what is broken and we will tell you what it takes.
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