Debugging Bad Rows in Athena: A Practical Guide for Snowplow Users

Snowplow's non-lossy pipeline architecture is distinguished by its ability to preserve malformed or invalid events as "bad rows" rather than discarding them, providing detailed error messages that help identify data quality issues. This approach is valuable for data teams rolling out new tracking systems or monitoring pipeline integrity over time. The tutorial outlines the process of querying and debugging these bad rows using Amazon Athena, a serverless SQL-based query service, and is designed for data engineers, architects, and analytics professionals familiar with AWS, Snowplow, and SQL. By creating an Athena table that maps to the bad row structure in S3, users can analyze error frequencies, preview types of errors, filter non-critical issues, and isolate specific recurring problems, which helps improve tracking reliability. The guide also suggests visualizing trends and error categories using tools like Amazon QuickSight or Redash and discusses recovery options such as Hadoop Event Recovery and custom JavaScript functions. Ultimately, Snowplow's method enhances data transparency and supports building more trustworthy data pipelines, allowing teams to maintain a reliable and high-integrity event stream.