3 Best Practices for monitoring Data Pipelines
How can Data Engineers effectively monitor Data Processes?
Data pipelines are an important component of modern data driven enterprises. They enable the efficient and effective transfer of data from various source systems to target systems like a Data Warehouse or a Data Lakehouse. For Data Engineers who build and manage these data processes, it is important to monitor these data streams, otherwise missing data or erroneous data may occur.
The monitoring these pipelines can be of course very challenging, as even small disruptions or errors can have a significant impact on downstream processes. In this article, I will present you three best practices for monitoring data pipelines.
Best Practice 1: Establish Key Performance Indicators (KPIs)
The first step in monitoring data pipelines is of course to establish KPIs. KPIs are measurable values that indicate how well a pipeline is performing. These values should be closely linked to the pipeline’s goals…
