Data Migration

Data migration sounds like copying data from A to B but is rarely that simple in practice. Source and target systems have different data models, value lists do not align one-to-one, historical data contains inconsistencies, and required fields in the target often have no equivalent in the source.

A serious migration runs in phases: source analysis (what do we have?), mapping design (how does it translate?), cleansing (what needs to be corrected first?), test migration (does it work technically?), parallel migration (does the data quality hold up functionally?), cutover (when do we switch?). Skipping phases leads to problems that surface late and expensive.

Pimcore supports migrations with bulk import APIs, configurable ETL workflows, and built-in validation rules. Data cleansing workflows can be prepared inside Pimcore before the migration, so no bad data is carried into the new world. Audit logs document every migration step for compliance and traceability.