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. In the previous post we’ve scratched the surface of how one can check data quality with Apache Spark . But the real complexity lies in the greater data quality landscape, which involves people and processes, not just the Spark clusters.
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