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Control-M + Astronomer
Your Astronomer workflow starts before an Airflow DAG ever runs — and it can fail when something upstream breaks. An Amazon S3 object arrives late. A dbt Cloud transformation misses its expected completion window. Control-M orchestrates the full chain: S3 data arrival, dbt Cloud transformation, Astronomer DAG execution, and Snowflake load.
common workflow issues
Does this sound like your week?
These aren’t edge cases. They’re the normal operating conditions for teams running Astronomer DAGs across multiple tools. Here’s how Control‑M handles each one.
UPSTREAM DELAY
Your 2:00 a.m. DAG is ready. The S3 data isn’t.
Control-M holds the Astronomer DAG until the required upstream data arrives, then launches it when the dependency is satisfied. The workflow follows actual data readiness instead of a disconnected clock schedule, preventing premature runs and avoidable downstream failures.
DAG FAILURE
One Airflow task failed. The downstream reporting chain is already waiting.
Control-M monitors Astronomer job status and can rerun the DAG, including rerunning only failed tasks. Downstream Control-M jobs remain governed by dependencies, preventing a failed DAG from silently propagating incomplete data into the rest of the enterprise workflow.
CROSS-TOOL DEPENDENCY
dbt finished late. Your Astronomer DAG still needs the right trigger.
Control-M models the dbt job and Astronomer DAG in one workflow, evaluates the upstream completion state, and starts the DAG only when its dependency is satisfied — removing brittle time offsets and manual coordination between independently scheduled platforms.
SLA RISK
The DAG is running, but the 7:00 a.m. delivery is slipping.
Control-M attaches SLA management to Astronomer jobs and tracks their contribution to the broader workflow. Teams can identify schedule risk in context and intervene before a delayed DAG pushes the end-to-end data delivery beyond its required business window.
FAILURE RECOVERY
The DAG failed at 3:17 a.m. Operations needs context, not another console.
Control-M surfaces Astronomer workflow status, results, output, and logs within the orchestration environment. Operators can identify the failure, abort when required, and coordinate recovery without reconstructing the surrounding dependency chain across multiple scheduling and monitoring tools.
Control‑M + Astronomer
Control‑M + Astronomer
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workload.types |
Airflow DAG execution · DAG reruns · failed-task reruns · parameterized DAG runs · Manual JSON execution · parallel Astronomer jobs |
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trigger.type |
upstream job completion · file arrival · API/event condition · time schedule · Control-M dependency · business calendar · manual execution |
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cross_tool.deps |
dbt Cloud job · Snowflake job · Databricks job · Amazon S3 file arrival · REST API call · managed file transfer |
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cloud.platforms |
Astro · Astronomer Software · AWS-connected workflows · Microsoft Azure-connected workflows · Google Cloud-connected workflows |
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error_handling |
DAG rerun · failed-task-only rerun · configurable failure tolerance · status polling · workflow abort · downstream cascade prevention · log retrieval |
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throughput |
multiple Astronomer jobs simultaneously per Agent · parallel DAG orchestration · parameterized executions · enterprise-scale cross-workflow coordination |
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observability |
Astronomer job status · workflow results · job output · log retrieval · SLA tracking · end-to-end dependency visibility · Control-M monitoring |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across Astronomer, dbt Cloud, Snowflake, Databricks, file transfers, and cloud services in a single job flow — with dependency tracking, SLA visibility, and automated recovery across all of them.
- Cross-tool dependency: dbt Cloud → Astronomer DAG → Snowflake load → analytics handoff
- Data-aware triggers: file arrival, API event, upstream job completion, schedule condition
|
Astronomer |
run DAG · rerun DAG · rerun failed tasks · pass JSON parameters · monitor status · retrieve logs |
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dbt Cloud |
trigger jobs · monitor completion · coordinate downstream dependencies |
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Snowflake |
orchestrate jobs · coordinate data processing · manage downstream dependencies |
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Databricks |
orchestrate jobs · coordinate processing · connect downstream workflows |
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Amazon S3 |
detect file arrival · gate downstream processing · coordinate ingestion |
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Managed File Transfer |
secure file movement · delivery dependencies · transfer monitoring |
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REST APIs |
invoke services · coordinate API-driven steps · connect external workflows |
airflow coexistance
Control‑M doesn’t replace your Airflow DAGs. It runs the layer above them.
The objection is common: “We’re already on Airflow.” The issue isn’t what Airflow does – it’s what happens before and after Airflow runs. That’s where pipelines actually fail.
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 PIPELINES
Monitor Astronomer DAGs in the full workflow context.
Astronomer provides visibility inside its Airflow environment, but production data pipelines often extend across many platforms. Control-M brings Astronomer execution into the same operational view as upstream and downstream jobs, giving teams end-to-end context across the workflow:
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Astronomer job execution status
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Workflow results and output
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Upstream and downstream dependencies
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Astronomer log retrieval
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Cross-platform workflow visibility
SLA ASSURANCE
Manage Astronomer DAGs against business delivery deadlines.
A successful DAG can still contribute to a missed business deadline when upstream processing starts late or downstream work runs long. Control-M connects Astronomer jobs to end-to-end SLA management so teams can manage the delivery outcome, not just DAG completion:
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End-to-end SLA tracking
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Astronomer SLA job association
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Cross-platform dependency monitoring
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Automated failure handling
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Business deadline visibility
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Coordinated failure recovery
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
