What purpose do health checks serve in a data pipeline?

Health checks in a data pipeline primarily serve to validate the quality of data over time. This involves regularly monitoring various aspects of the data as it moves through the pipeline to ensure that it meets certain quality standards. This includes checking for issues such as missing values, data corruption, or inconsistencies that could compromise the accuracy and reliability of the insights generated from that data.

By incorporating health checks, data teams can promptly identify any anomalies or degradation in data quality, enabling them to take corrective action before the data is used for analysis or reporting. This ongoing validation is crucial because it helps maintain the integrity of decision-making processes that rely on this data.

Other objectives like managing costs, implementing new features, or optimizing speed are important considerations in the operation of a data pipeline; however, they do not directly relate to the fundamental purpose of health checks, which is centered around maintaining data quality over time.