Data pipeline monitoring: What it is and how to implement data quality testing

Data pipelines can be disrupted by issues such as unexpected schema changes, bad code updates, and data drift, which can result in broken transformations and inaccurate insights. Data pipeline monitoring plays a crucial role in maintaining data integrity and reliability as it moves through various stages like ingestion, transformation, and storage. By adopting a shift-left or upstream approach, problems like schema changes and data anomalies can be identified early, preventing costly fixes and ensuring accurate business decisions. This proactive monitoring strategy involves using tools such as data diff, schema change detection, metrics monitoring, and data tests to catch issues before they affect downstream components like reports and dashboards. Traditionally, monitoring has been reactive, but shifting monitoring efforts upstream helps maintain data quality from the start of the pipeline, minimizing disruptions and ensuring reliable insights.

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