Where SQACE fits
What SQACE secures that others do not test.
SQACE replaces neither your data-quality monitoring nor your regression tests. It closes the gap between them: the question of whether the processing produced the functionally correct result, across systems and readable for the business side.
How it differs
Three questions, three tools.
DQ monitoring, regression tests and SQACE answer different questions. None replaces the others; together they cover more.
DQ monitoring
Shows that something looks off: nulls, outliers, threshold breaches. Answers “does the data look good?”, not “is the result functionally correct?”.
Regression testing
Shows that nothing changed that should not have. Answers “is it still as before?”, but finds no errors that were always wrong.
SQACE
Computes the expected result from your rule set as an independent second calculation and compares it against the actual. Answers “did the processing produce the functionally correct result?”.
In comparison
| SQACE | DQ monitoring | Classical ETL testing | In-house scripts | |
|---|---|---|---|---|
| What is tested? | The processing | The data | The ETL route | Individual cases |
| Core question | Is the result correct? | Does the data look good? | Is the SQL correct? | Is this case correct? |
| Method | Rule-based expected-vs-actual | Anomaly detection, thresholds | SQL comparisons | Hand-written SQL/code |
| Cross-system | Yes, platform-independent | Within the platform | Limited | Project-dependent |
| Who uses it? | Business + IT | IT | IT | Individual developers |
| Deviations across runs | Classified, reusable | Per run again | Manual | Manual |
| Migrations | Mapping verification, full volume | Not intended | Limited | Labour-intensive |
| Audit-proof evidence | Built in | Partial | Manual | Rare |
Already have DQ in your stack?
Platform checks watch the data. SQACE checks the result.
Modern data platforms ship their own data-quality features, such as Delta Live Tables expectations in Databricks or data metric functions in Snowflake, alongside DQ observability tools. They check rules and anomalies within one platform, and they are useful there. Often they are already included in the stack.
But they do not answer one question: did the processing produce the functionally correct result? SQACE computes the expected result from your rule set as an independent second calculation and compares it against the actual output, across system and platform boundaries and migrations, including sources outside the platform, readable for the business side and with deviation classification across multiple runs. This complements platform checks rather than replacing them.
How it fits
SQACE verifies, your toolchain orchestrates.
SQACE replaces none of your existing tools. It adds the missing verification layer: an instance outside the stack that the processing was built with. Your pipeline produces the actual, SQACE compares it against an independently computed expected result, and your test management gets release, deviation and audit trail.
How SQACE connects and runs securely (data sources, test management, CI/CD, operations): Security & architecture.