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Control-M + Databricks
Your Databricks workflow starts before a parameterized notebook task ever executes within its job run — and it can fail when something upstream breaks. An Amazon S3-backed Delta Lake table partition never receives its upstream records before the job's notebook task resolves its source path. A dbt model run overruns its warehouse query timeout before materializing its transformed table for the job's input mount. Control-M orchestrates the full chain: Amazon S3 ingestion, dbt transformation, Databricks execution, and GCP BigQuery delivery.
common workflow issues
Does this sound like your week?
UPSTREAM DELAYS
Your Databricks job is scheduled. The source data still isn't ready.
A scheduled job starts before upstream ingestion, file transfers, or ETL processes complete, leading to failed notebooks or incomplete datasets. Control-M waits for verified upstream completion, evaluates dependencies, and launches Databricks only when data is ready.
FAILED DEPENDENCIES
Spark finished with errors. Downstream analytics kept running anyway.
A failed Spark process or upstream workflow can trigger incomplete or inaccurate downstream processing. Control-M detects exit status, prevents failure cascades, automates configurable recovery, and resumes dependent workflows only after successful remediation.
CROSS-PLATFORM FLOWS
One workflow spans Databricks, dbt, APIs, cloud storage, and SQL.
Production pipelines rarely live inside a single platform. Control-M orchestrates dependencies across Databricks, cloud storage, data integration tools, databases, APIs, and analytics platforms from a single workflow with centralized visibility and control.
SLA PRESSURE
The morning dashboard deadline is approaching. Jobs are still running.
When upstream delays threaten reporting deadlines, teams need more than job status. Control-M predicts SLA risk, identifies critical-path delays, alerts operators before breaches occur, and prioritizes recovery actions to keep business commitments on track.
FAILURE RECOVERY
A notebook failed overnight. Nobody noticed until business hours.
Manual recovery wastes valuable time and delays downstream consumers. Control-M automatically detects failed Databricks executions, applies configurable retry policies, triggers notifications or remediation workflows, and restarts processing from the appropriate point instead of rerunning entire pipelines.
INTEGRATION FACTS
Control‑M + Databricks
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workload.types |
Databricks Jobs · Databricks Notebooks · Databricks Workflows (multi-task jobs) |
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trigger.type |
file arrival (Amazon S3 · Azure Data Lake Storage · Google Cloud Storage) · upstream job completion · REST API/webhook · time schedule · event trigger · manual trigger · job exit code |
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cross_tool.deps |
Apache Airflow DAG trigger · dbt Cloud run completion · Fivetran sync completion · Azure Data Factory pipeline · REST API call · file transfer completion |
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cloud.platforms |
AWS · Microsoft Azure · Google Cloud Platform · Control-M SaaS · Control-M on-premises |
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error_handling |
configurable retry policies · downstream dependency control · automated job hold on upstream failure · failure notifications · SLA pre-breach alerting · PagerDuty · Slack |
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throughput |
high-volume batch processing · parallel job execution · distributed Spark workloads · scheduled data pipelines · large-scale data transformation · event-driven orchestration |
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observability |
centralized job monitoring · SLA tracking with breach prediction · dependency lineage visualization · execution audit trail · Datadog integration · Splunk integration · SIEM-compatible events |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across Databricks, Apache Airflow, dbt Cloud, Fivetran, cloud storage, APIs, and cloud services in a single job flow—with dependency tracking, SLA visibility, and automated recovery across all of them.
- Cross-tool dependency: File arrival → Fivetran sync → dbt Cloud transformation → Databricks Job → Power BI dashboard refresh
- Data-aware triggers: File arrival · API event · dbt Cloud completion · Databricks Job completion
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Databricks |
Job execution · Workflow orchestration · Notebook execution · Multi-task workflow coordination · Job status monitoring |
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Apache Airflow |
DAG triggering · Dependency coordination · Execution status tracking · Cross-platform orchestration |
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dbt Cloud |
Run completion detection · Transformation dependency management · Downstream workflow triggering |
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Fivetran |
Sync completion monitoring · Data ingestion orchestration · Pipeline dependency management |
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Cloud Storage (Amazon S3 · Azure Data Lake Storage · Google Cloud Storage) |
File arrival detection · Event-based triggering · Data availability validation |
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REST APIs |
Workflow initiation · Status polling · Event-driven orchestration · External system integration |
|
Power BI |
Dashboard refresh orchestration · Analytics pipeline completion · Reporting workflow automation |
airflow coexistance
Control-M doesn’t replace your Airflow DAGs. It runs the layer above them.
Airflow manages its DAG. Control-M manages everything surrounding it.
AIRFLOW HANDLES
DAG-level orchestration inside the data pipeline
- DAG-level task orchestration within data pipelines
- Python operators, sensors, and task dependencies
- Execution graph for jobs that run inside your pipeline
- Manages retries within a single DAG context
control-m adds
The coordination layer around your DAGs
- Coordination layer around DAGs — triggers Airflow based on upstream conditions: file arrivals, API events, other tool completions
- Tracks each DAG’s SLA contribution across the full end-to-end workflow, not just its own routine
- Manages failure recovery when upstream dependencies fail before Airflow even starts
- Existing DAGs don’t need to be rewritten or migrated
MONITOR WORKFLOWS
Monitor Databricks workflows from a single operational view
Databricks provides visibility into individual jobs and workflows, but production pipelines typically span multiple platforms. Control-M delivers centralized monitoring across your end-to-end workflow, enabling operators to quickly identify issues, understand dependencies, and take action before downstream processes are affected:
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End-to-end workflow visibility
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Job status and runtime history
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Cross-platform dependency tracking
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SLA risk prediction
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Centralized operational dashboard
AUTOMATED RECOVERY
Recover Databricks workflows automatically before SLAs are missed
When a Databricks job fails, the impact often extends well beyond the platform itself. Control-M detects failures, applies configurable recovery actions, and coordinates dependent systems automatically to reduce manual intervention and keep production workflows moving:
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Configurable retry policies
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Dependency-aware recovery
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Automated operator notifications
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Failure isolation and restart
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SLA breach prevention
