Challenge
Invisible runs a Databricks medallion architecture processing data across raw, cleaning, and business-ready layers. As the platform scaled, pipeline failures went undetected and cloud costs accumulated without attribution.
Solution
We built a data-quality monitoring system across all three medallion layers with automated alerting, introduced local testing before production deployment, implemented hash-based deduplication, and delivered granular cost dashboards.
Results
- Pipeline anomalies detected automatically across Bronze, Silver, and Gold layers
- Production incidents from untested code eliminated
- Reliable deduplication across datasets with no primary keys
- Full cost attribution at job and cluster level
Technology
Python · PySpark · Databricks · Temporal · Notebooks · Delta Lake
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