Warehouse Migration
Moving analytics from one platform to another, where the difficulty is almost never the data and almost always the accumulated logic and consumers.
The data copy is the easy part and the part every plan is built around. The work is everything attached to it: thousands of transformation objects with dialect-specific SQL, reports and extracts pointing at the old system, scheduled jobs, permissions models that do not map cleanly, and downstream consumers nobody has an inventory of.
The technique that de-risks it is parallel run with reconciliation. Both platforms are loaded from the same sources for a period, and outputs are compared automatically — row counts, aggregate totals, and value-level comparison on key measures. Differences are then investigated one at a time, and a meaningful proportion turn out to be defects in the old platform that someone has been reporting for years, which is a politically delicate discovery and needs to be planned for.
Two decisions dominate the cost. Whether to lift and shift the existing logic, which is faster and carries every accumulated flaw across, or to remodel, which is the right long-term answer and routinely doubles the timeline. And how to handle consumers: a compatibility layer preserving old object names lets consumers migrate on their own schedule rather than in a single coordinated cutover that will not happen.
Decommissioning is the step that gets cancelled once the new platform works, leaving both running. Fund it as part of the migration, or the saving never arrives.