Reference workflows, reusable logic, and implementation guidance to monitor dependent Databricks jobs, check daily refresh SLAs across Bronze, Silver, Gold, and other data layers, and send automated status notifications to stakeholders.
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Modern data platforms often depend on multiple scheduled workflows across Bronze, Silver, Gold, and business-ready layers. When one dependency fails or misses its SLA, downstream dashboards, reports, and analytics processes can break silently.
DataTheta’s Databricks Job Monitoring Accelerator helps teams track dependent workflows, compare job runs against declared layer dependencies, send failure alerts as soon as issues are detected, and send success notifications once every dependency for a data layer has completed.
AI systems in production
Avg. time to first outcome
Forecast accuracy improvement
Faster decision cycles
Revenue influenced by AI
Manual processing eliminated
Create and maintain Unity Catalog audit tables for workflow run logs and notification history.
Pull recent Databricks job run history through the Jobs REST API and keep the latest run status for each monitored workflow.
Compare completed and failed runs against declared workflow dependencies to determine daily refresh status for each data layer.
Send HTML failure emails as soon as a monitored dependency fails and avoid duplicate alerts by checking the notification log.
Send success emails once every dependency for a layer has completed, then log the notification for audit and tracking.
Explore reusable accelerators for data quality, workflow monitoring, governance, AI readiness, reporting automation, and enterprise data operations.
Use DataTheta’s Databricks Job Monitoring Accelerator to track dependent workflows, detect refresh failures early, send stakeholder notifications, and maintain auditable monitoring history in Unity Catalog.
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