Data Warehouse Migration for a Growing Coffee Chain

The situation

A fast-growing coffee chain was running their analytics on Redshift. The warehouse worked, but it required cluster provisioning and active management — overhead that made sense at enterprise scale but was harder to justify for a company whose query patterns were variable and whose internal data team was lean.

The cost and complexity of Redshift cluster management were both friction points. The team wanted a warehouse that scaled with usage rather than requiring them to size infrastructure in advance, and a BI layer that kept metric definitions in code rather than scattered across a GUI.

What we built

CorrDyn migrated the warehouse from Redshift to MotherDuck, a serverless analytical database built on DuckDB. The move eliminated cluster management entirely. Compute scales automatically, and the cost structure matches actual query volume instead of reserved capacity.

The migration required refactoring the existing dbt models. Redshift-specific SQL syntax doesn’t run cleanly on DuckDB, so we rewrote the transformation layer model by model, validated output equivalence at each step, and resolved accumulated technical debt in the process. Each model was tested against its Redshift equivalent before the cutover.

For the BI layer, we deployed a code-first analytics tool that connects to dbt and stores metric definitions in version control alongside the models. Dashboards were built on top of the newly refactored transformation layer and deployed for the team after the warehouse migration was stable.

What changed

The team no longer manages Redshift clusters or provisions compute capacity ahead of expected query load. Infrastructure overhead dropped and costs better reflect actual usage. The cost optimization came alongside a cleaner transformation layer: the dbt refactoring removed technical debt that had accumulated since the original Redshift setup. Metric definitions now live in version control alongside the dbt models, so there is no divergence between what the models compute and what the reports display.