Case study · Data Quality
Data Quality Report Powered by AI
They needed to centralize all the quality data coming from Azure Data Factory jobs, Databricks jobs and table schemas.
The situation
They couldn’t detect data quality issues until the corrupted data arrived to the BI layer.
What we built
Different ETLs getting data from Azure Data Factory jobs, Databricks Jobs and quality rules using DQX framework. We empowered this platform with AI to make the reports and the client needs more verbose and easy going.
Services involved
Next steps
Data quality monitoring requires centralized visibility.
If your quality issues surface too late in the pipeline, we can build the monitoring platform and AI-powered reporting that catches problems before they reach BI.