We’ve been dumping raw logs and IoT sensor data into our S3-based data lake for a year, and now our analysts can’t find anything. There’s no metadata, and versioning is a nightmare. What are the mandatory governance steps or tools we should implement now to catalog this data and ensure quality without slowing down our ingestion pipelines?
3 answers
The transition from a lake to a swamp usually happens because of a lack of a "Schema-on-Read" strategy and poor tagging. In my previous role at a logistics firm, we solved this by implementing a mandatory metadata tagging policy at the ingestion layer. We used AWS Glue for automated crawling and Athena for ad-hoc querying. By enforcing a data contract where every producer must provide a YAML file describing the schema, we regained control. I also highly recommend looking into a dedicated Data Catalog tool like Alation or Collibra to give non-technical users a UI to search for assets.
Are you currently using any automated data quality profiling tools to check for null values or schema drifts before the data actually hits your silver or gold storage zones?
You need to move to a Medallion Architecture. Separate your data into Bronze (raw), Silver (cleansed), and Gold (business-ready) zones to create a clear flow of quality.
Mark is spot on. The Medallion Architecture is the industry standard for a reason—it provides a logical structure that makes auditing and data lineage much easier to manage.
Steven, we aren't doing any pre-ingestion checks yet, which is exactly why we're in this mess. We just have a Python script moving files. I'm curious, would you suggest doing those checks in the Spark pipeline itself, or should we use a separate service like Great Expectations to validate the data? We want to keep latency low but accuracy high.