Migration testing
Validate mappings early, cut over without surprises.
SQACE accompanies data migrations from mapping definition through to full-population verification before cut-over, with a demonstrable audit trail for MaRisk AT 9, BAIT/VAIT, DORA and NIS2.
30 minutes, on your own use case.
The problem
Migrations get checked for business correctness too late.
Data migrations rarely fail at the transfer step. They fail on unclear or misunderstood business mappings, and that often surfaces only shortly before cut-over: valuation logic was interpreted differently, column meanings overlap, or special cases from the legacy system are not represented.
Corrections under time pressure are expensive, and cut-over delays become likely. With historical populations there is an added problem: samples systematically under-represent special cases. The critical records only appear after go-live in reporting, often in the first quarter-end closings or in supervisory reports.
The SQACE solution
From the mapping phase to the full population.
SQACE starts early, at mapping definition, and checks the full population at the end, across three phases with a complete audit trail per run.
Approach
Three phases, one rulebook.
Mapping definition
Business experts describe the translation from source to target structures as a rule-based mapping. SQACE computes the mapping on a small, business-representative data set and shows the expected result. Inconsistencies become visible early.
Iteration with deviation analysis
With every migration iteration, SQACE compares the actual output with the expected result computed from the mapping. Deviations are classified. Business experts see whether the migration logic follows the defined mapping.
Full-population verification in the final phase
Before cut-over, SQACE runs across the complete population. Remaining outliers are handled before go-live, not in live operation.
Application migrations (e.g. Salesforce → SAP)
SQACE checks whether contacts, opportunities, activities and custom fields are correctly transferred into the new data model.
New trading systems with historical positions
Valuation snapshots from the legacy system are compared with an independent calculation in the new system, and deviations are reported.
Legacy DWH modernization on cloud platforms
When moving from Teradata or Oracle to Snowflake, Databricks or BigQuery, SQACE checks the business identity of the reporting layers in the expected-vs-actual comparison.
In concrete terms
- Business acceptance from the start through mapping validation on a concrete data example.
- Lower cut-over risk through full-population verification before go-live.
- Reusable rulebooks for subsequent migrations and modernizations.
At a glance
| Phase | With SQACE | Classical approach |
|---|---|---|
| Mapping definition | Rule-based mapping with expected results by example | Excel documentation, manual reviews |
| Iteration | Representative full runs with classification | Samples |
| Final phase | Full-population verification before go-live | Cut-over risk |
| Audit trail | Complete per run | Scattered across the project |
Compliance
Compliance evidence as a by-product.
Data migrations in banking fall under MaRisk AT 9 and BAIT/VAIT; in critical-infrastructure contexts NIS2 and DORA apply as well. SQACE delivers a complete audit trail per migration phase, with tested records, applied rules and classified deviations, so supervisory reviews can be prepared without after-the-fact reconstruction.
How SQACE spares production systems and isolates data is described in Security & architecture.
FAQ
Frequently asked questions
How early should SQACE join the project?
Ideally in the design phase, at mapping definition. A later start is possible but costs iterations.
Are samples enough for mapping validation?
For mapping validation, small, well-chosen data sets are sufficient. Before cut-over, a full-population run is recommended.
How many fields can realistically be mapped?
Migration projects with several thousand mapping rules are documented in production use.
How are system-specific data types handled?
SQACE works on a canonical representation. Conversions, rounding rules and time zones are made explicit in the mapping and versioned.
Is the source system put under load?
No. SQACE works on extracted records or replications. Production systems remain untouched.
Is SQACE also suitable for sub-ledger or merger projects?
Yes. Merger migrations with dual entities and sub-ledger transitions are among the typical fields of use.