How to implement effective Data Lineage and Observability in a multi-cloud environment?

All answers to this question.

You need to move beyond simple "logging" and into active "Data Observability." Tools like Monte Carlo or Bigeye are great because they use ML to detect anomalies in your data volume or schema changes without manual thresholds. For lineage, we integrated OpenLineage with our Airflow DAGs. It gives us a visual map of how data flows from our S3 buckets in AWS into our Snowflake instance. Having this unified view has reduced our Mean Time to Resolution (MTTR) for data issues by nearly 60% since last quarter.

   Answered 2025-10-14 by Susan Martinez

Are you finding that the metadata from your cloud-native tools like AWS Glue or Azure Data Factory is sufficient, or are they too "walled off" from each other to be useful?

   Answered 2025-11-02 by Richard Lewis

• Richard, they are definitely too siloed. Glue doesn't know what happened in Data Factory, and vice versa. We’ve had to implement a third-party catalog like Atlan to act as the "control plane" that stitches the metadata together into a single, searchable business glossary for our users.

     Commented 2025-11-10 by Steven Hall

Look into the "Medallion Architecture" (Bronze, Silver, Gold). It naturally creates checkpoints in your lineage that make it easier to debug where the data quality actually dropped.

   Answered 2025-12-01 by Linda Garcia

• The Medallion approach is a lifesaver. It’s much easier to tell a stakeholder "the Silver layer is clean, but the Gold aggregation failed" than just saying "the data is wrong."

     Commented 2025-12-04 by Patricia Moore