Data Pipeline Testing
Validating data integrity across every stage of your pipeline.
What working with us on this looks like in practice.
What this engagement covers.
Data pipeline failures are silent, bad data flows downstream without errors before anyone notices. We build assertion suites covering schema correctness, row counts, null rates, referential integrity, and transformation logic.
Source and target schemas verified against contracts on every pipeline run.
Input-to-output record counts compared with tolerance thresholds.
Null rates, duplicate detection, and range validation per column.
Business rules verified with known input/output pairs.
Upstream schema changes detected before they corrupt downstream tables.
Pipeline completion time and data freshness tracked against defined SLAs.
Data pipeline testing, answered.
What is data pipeline testing?
It verifies that data arriving in your warehouse is complete, correctly transformed and on time. Checks cover schema drift, row counts, referential integrity and business rules at each stage of the pipeline.
How is it different from testing an application?
Applications fail loudly; pipelines fail quietly. A broken transformation still produces a table, just with wrong numbers, so the tests have to assert on data quality itself rather than on whether the job completed.
What do you deliver?
Automated data quality checks running on every pipeline execution, alerting tied to freshness and volume thresholds, and a dashboard showing where in the pipeline a failure started rather than only that it failed.
Make your next releaseuneventful.
Book a free 30-minute quality audit. We'll review your stack and show you exactly where the risk is hiding, no pitch deck required.