Out of the laptop
SQL scripts on one person's machine become readable steps anyone can review and run.
Data & ETL engine
Catch bad loads before your dashboards do
The Data & ETL engine runs on the Qventis Engine, so the same plain-English language that drives your screens also reconciles source and target, catches nulls, duplicates and schema drift, and checks reports against the warehouse.
- Reconcile every load
- No AI model reads your rows
- Compiles to SQL you can read
Compare 'staging.orders' with 'dw.fact_orders' on 'order_id'
Check that row counts match for yesterday's load
Check that 'order_id' is unique in 'dw.fact_orders'
Check that the schema of 'dw.fact_orders' matches 'baseline'
Data checks the whole team can read
Runs in your warehouse
Steps compile to SQL and run where the data sits. Only results and a sample of mismatches come back.
Dashboard to source
One test reads a value off a BI report and checks it against the warehouse.
How the data engine works
What each check catches
The checks data teams write by hand, as steps.
- Reconciliation: missing, extra or changed rows between source and target
- Row counts: partial loads and silent filter changes
- Nulls and uniqueness: broken joins, duplicate loads and bad merge keys
- Schema drift: renamed, dropped or retyped columns before reports break
The work stays put
Large tables are compared where they sit, which keeps runs fast and data where your security team expects it.
- Credentials come from your vault by reference
- Run checks after a load job and fail the pipeline on a broken one
The last mile of trust
A pipeline can be correct and a report still wrong because of a filter, a join or a stale extract.
Open the 'Sales dashboard' report
Remember the 'Revenue' tile as {{report.revenue}}
Check that {{report.revenue}} equals the sum of 'amount' for last month
App and data together
Tables, columns and entities sit in the App Model next to the screens that display them.
- See which data checks and UI tests a column change affects
- Data results land in Quality View beside functional results
- A release decision covers the app and the data it reports
Questions
Do I need to write SQL?
No. Steps compile to SQL for you, and you can read the generated query anytime.
Can one test cover a screen and a table?
Yes. A step can read a value off a desktop or web screen and the next step can check it against the warehouse.
See one pipeline reconciled end to end
Bring a load job and the dashboard it feeds, and leave with both checked in one test.