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Control-M + GCP Batch
Your GCP Batch workflow starts before a script or container ever executes — and it can fail when something upstream breaks. A Cloud Storage object never arrives. A Dataflow job runs past its processing window. Control-M orchestrates the full chain: Cloud Storage arrival, Dataflow processing, GCP Batch execution, and BigQuery loading.
common workflow issues
Does this sound like your week?
These aren’t edge cases. They’re the normal operating conditions for teams running GCP Batch jobs across multiple tools. Here’s how Control-M handles each one.
UPSTREAM DEPENDENCIES
Your Cloud Storage input is late. The Batch job cannot safely start.
Control-M waits for the required upstream condition before releasing the GCP Batch job, then coordinates downstream dependencies from the resulting job state — avoiding brittle time-based scheduling and preventing incomplete inputs from cascading through the workflow.
SPOT INTERRUPTION
A Spot VM disappears mid-run. Your processing window keeps shrinking.
GCP Batch can retry failed tasks, including failures caused by Spot VM preemption. Control-M monitors the overall GCP Batch job and coordinates recovery and downstream execution based on its resulting status, keeping the wider workflow under control.
FAILURE PROPAGATION
The Batch job fails. BigQuery must not load incomplete results.
Control-M detects the GCP Batch job outcome and holds dependent processing when the required completion condition is not met. Downstream BigQuery, transfer, or application jobs remain protected until the failure is resolved and the workflow can continue safely.
SLA RISK
The job is still running. Your downstream delivery deadline is approaching.
Control-M brings the GCP Batch job into the end-to-end service workflow and supports SLA monitoring across dependent jobs. Operations teams can see when delayed processing threatens delivery and intervene before the overall business workflow misses its target.
OPERATIONS VISIBILITY
GCP has the logs. Operations still needs the whole workflow context.
Control-M monitors GCP Batch status, results, and output alongside the other jobs in the workflow. With Cloud Logging enabled, Batch logs are saved in the Control-M Monitoring domain alongside job status and output, reducing context switching during investigation and recovery.
Control‑M + GCP Batch
Control‑M + GCP Batch
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API and automation capabilities |
Control-M Automation API · Control-M Web · Job Batch · ConnectionProfile Batch · advanced JSON job definition · configurable status polling |
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Deployment models & infrastructure flexibility |
Control-M · Control-M SaaS · Linux Agent plug-in · Windows Agent plug-in · script workloads · container workloads · Standard or Spot VMs · machine types or instance templates |
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Security posture |
GCP service account authentication · IAM role-based authentication · secure connection profiles · centralized connection profiles · local connection profiles |
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Incident response & MTTR enablement |
configurable Max Task Retry Count · job status monitoring · results and output monitoring · Cloud Logging support · SLA job attachment · dependency-based cascade prevention |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across GCP Batch, Cloud Storage, Dataflow, BigQuery, Airflow, 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: Cloud Storage → Dataflow → GCP Batch → BigQuery
- Data-aware triggers: file arrival, API event, upstream job completion, Batch job status
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GCP Batch |
script execution · container execution · status monitoring · output monitoring · SLA coordination |
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Cloud Storage |
file arrival · upstream dependency · downstream release |
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GCP Dataflow |
job execution · status tracking · dependency coordination |
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BigQuery |
query execution · downstream processing · dependency coordination |
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Airflow |
DAG orchestration · status tracking · cross-tool dependencies |
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Managed File Transfer |
secure transfer · file arrival detection · delivery coordination |
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 WORKLOADS
Monitor GCP Batch status and output in context.
GCP Batch exposes job status and Cloud Logging data, but production support often spans many services. Control-M puts GCP Batch execution into the same operational view as upstream and downstream jobs, giving teams end-to-end context for troubleshooting:
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GCP Batch execution status
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Job results and output
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Upstream and downstream dependencies
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Cloud Logging visibility
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End-to-end workflow monitoring
SLA ASSURANCE
Protect delivery SLAs beyond the GCP Batch job.
GCP Batch manages execution of its compute workload; it does not own the delivery commitment of the surrounding enterprise process. Control-M connects Batch execution to end-to-end workflow dependencies and SLA management so teams can manage the complete service outcome:
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End-to-end SLA tracking
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Cross-platform dependency visibility
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Delayed-workflow risk detection
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Controlled downstream execution
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Centralized operations monitoring
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
