The best data quality framework for senior platform engineers – Databand

In many ways, you’re only ever as good as your last delivery, and for many of us, continuous delivery means continuous scrutiny. You have to keep up quality, but also the perception of quality, because once the data trust is broken, your job becomes much more difficult. 

That’s why any organization that considers data important to the functioning of its business—whether internal consumers or external—needs to be practicing data quality management and implementing a data quality framework. This is what it sounds like: Developing repeatable, ideally automatic processes and patterns to ensure that the data entering your system and being delivered downstream is what you and your consumers expect.

And as you senior data engineers well know, understanding those expectations is half the battle. Much of the other half is spent translating those expectations into tracking and alerting that will help you find and fix issues in complicated ingestion processes.

In this guide, we share strategies to ensure that data quality management isn’t simply layered on top of your existing hard-coded processes, but is built into every DAG. To manage it well, you need to detect anomalies long before low-quality data enters your transformation layer.