Strategies for Data Quality with Apache Spark

In fact, many data teams are guilty of overlooking critical questions like “Are we actually monitoring the data?” after deploying multiple pipelines to production. They might celebrate the success of the first pipeline and feel confident about deploying more. Still, they need to consider the health and robustness of their ETL pipeline for long-term production use. This lack of foresight can lead to significant problems down the line and undermine trust in the data sets produced by the pipeline.