Data Warehouse Migration for a Team Stuck on a Legacy Platform
The legacy warehouse worked. It also cost more than the rest of the data stack combined and the vendor was slow-rolling features the team needed.
An enterprise team operating a legacy data warehouse from one of the established vendors. The platform worked. It also represented a substantial portion of the organization's annual data spend, the vendor's release cadence had slowed, and the features the team needed for their next stage of work were either unavailable or available at additional cost.
The leadership had been considering migration for several years and had repeatedly deferred because the migration cost looked higher than the recurring savings appeared to justify. The cost calculation had changed as the organization's data volume continued to grow under the legacy platform's per-volume pricing. The break-even point on migration had crossed; the deferral was no longer the rational choice.
A migration to Snowflake — selected after a comparative analysis against BigQuery and Databricks — executed in a phased approach that kept the legacy platform operational throughout.
The migration plan started with an inventory of what was actually in use. Significant portions of the legacy platform's footprint turned out to be deprecated objects, datasets that had been replaced but not retired, and reports that hadn't been opened in years. The retirement of these — performed before migration — reduced the actual migration scope substantially.
For the active footprint, schema and data were migrated in waves prioritized by interdependence and by business criticality. Each wave migrated a coherent set of datasets together so that the dependent transformations and reports could be migrated alongside.
ETL and transformation work was rebuilt in dbt running against Snowflake. The previous platform's stored procedures and scripts were translated; in many cases the rewrite produced cleaner, more maintainable transformations than the originals.
Reports and dashboards were migrated in parallel with their underlying data. Each report was tested against both the old and new platforms during the cutover window to verify equivalence; the cutover happened only after the new version had been validated.
The legacy platform was kept operational throughout. Each migration wave reduced its scope; the platform was decommissioned only after the final wave had completed and validation periods had passed without issues.
Cost monitoring was instrumented from the first day on Snowflake so the team could manage the new platform's economics actively rather than discovering them at the first invoice.
The migration completed within the planned timeline. Annual platform spend dropped substantially relative to the legacy platform's run rate. The team gained access to the modern features that had motivated the migration. The organization's data velocity improved as the new platform's tooling ecosystem opened patterns the legacy platform hadn't supported.