Data engineers running Apache Spark jobs on Amazon EMR face a persistent challenge: understanding how data moves through Spark pipelines as it’s transformed, joined, and written to downstream tables . Tracking these transformations manually requires examining job logs, reviewing code, and piecing together transformation logic across multiple sources. As pipelines scale, this process becomes complex. The visibility gap affects key business activities: troubleshooting data quality issues takes longer – impact analysis for schema changes requires more effort – and compliance audits need extensive documentation of data provenance.
This is a companion discussion topic for the original entry at https://aws.amazon.com/blogs/big-data/capture-data-lineage-of-amazon-emr-spark-jobs-into-amazon-sagemaker-unified-studio/