ETL Testing Services

The problem

A dashboard can look confident while the pipeline dropped a day, doubled a join, or mapped a status wrong. ETL testing services check the path from source to target: counts, keys, transforms, and the late-arriving row — before finance or a customer treats the number as true.

This is database testing with a pipeline in the middle. It is not “we built your warehouse.”

What this page will not claim

• Partnerships with Informatica, Fivetran, dbt, Snowflake, BigQuery, or Databricks
• That we design your warehouse, write your production jobs, or own your cloud bill
• Invented “99.9% data quality” scores
• Coverage of streaming, CDC, or every connector unless you name it

What’s at risk without it

• A report that is a day behind and nobody noticed
• A transform that silently nulls a required field
• Duplicates after a replay or a late source file
• A schema change upstream that the job “succeeded” through

What we test

• Source-to-target counts and key reconciliation on the tables you name
• Transform rules you can write down (or that live in the job we can read)
• Nulls, types, and referential checks that matter to the report
• Incremental vs full-load behaviour, including a replay, when you have a safe environment
• The job’s failure mode: does a bad file stop the load or write garbage?

Related: API testing when the extract is an API, database testing for the store itself.

Our approach

1. Name sources, targets, and the grain of the table (what is a row?)
2. Get read access to both sides and a way to trigger or inspect a run
3. Compare samples and totals — not a vibe check on a dashboard screenshot
4. Log the rule that failed, the keys, and the run id
5. Re-run after the next job or schema change

We need a non-production pipeline or a read-only production check you explicitly scope. We do not “fix the SQL in prod” as a side quest.

Deliverables

• Reconciliation notes for the tables in scope
• Defects with keys, expected vs actual, and the run
• A list of rules we could not verify (undocumented transforms)

Suitable for

Teams who ship a warehouse, a lakehouse, or a nightly load that someone else treats as finance-grade.

FAQs

Is this the same as database testing?

Database testing is the store. ETL testing is the movement and the transform. We often do both on the same engagement.

Do you need the job source code?

It helps. A written mapping and two queryable ends can be enough for a first cycle.

Will you certify the warehouse?

No. You get evidence against the rules in scope.