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Control-M + GCP Vertex AI
Your GCP Vertex AI workflow starts before a Vertex AI pipeline run is ever invoked against its configured compute cluster — and it can fail when something upstream breaks. A GCP BigQuery training dataset export never completes its table snapshot before the pipeline run's data ingestion component resolves its source URI. A GCP Dataflow feature engineering job overruns its execution deadline before writing its transformed records to the pipeline run's training data URI. Control-M orchestrates the full chain: GCP BigQuery ingestion, GCP Dataflow transformation, GCP Vertex AI execution, and GCP Cloud Storage delivery.
common workflow issues
Does this sound like your week?
These aren’t edge cases. They’re the normal operating conditions for teams running Vertex AI pipelines across multiple tools. Here’s how Control‑M handles each one.
MODEL TRAINING
Last night’s training run started. The feature dataset never arrived.
Control-M validates upstream dependencies before launching Vertex AI jobs. File arrivals, ETL completion states, and data quality checks are evaluated automatically, preventing wasted compute cycles and failed model training runs.
SLA RISK
The batch scoring workflow is already 40 minutes behind.
Control-M continuously tracks workflow progress across ingestion, transformation, and Vertex AI execution stages. Predictive SLA monitoring identifies risk before deadlines are missed and triggers alerts or automated remediation actions.
CROSS-TOOL DEPENDENCIES
Dataproc finished. Vertex AI is still waiting for a manual trigger.
Control-M detects upstream completion events and immediately launches dependent Vertex AI workflows. Event-driven orchestration eliminates polling, scheduling gaps, and manual intervention while maintaining full dependency visibility.
FAILURE RECOVERY
One preprocessing task failed. The entire pipeline stalled overnight.
Control-M applies configurable retry policies, exception handling, and downstream cascade prevention. Failed tasks are isolated, recovery workflows are triggered automatically, and unaffected processes continue executing where appropriate.
MODEL OPERATIONS
The model deployed successfully. Nobody validated downstream delivery.
Control-M coordinates post-deployment activities including validation tests, notification workflows, data exports, and application handoffs. Every step is tracked as part of a single end-to-end production workflow.
INTEGRATION FACTS
Control‑M + GCP Vertex AI
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workload.types |
model training · batch prediction · AutoML pipelines · custom training jobs · feature engineering workflows · MLOps pipelines · model deployment · notebook execution |
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trigger.type |
file arrival (Cloud Storage) · Pub/Sub event · API/webhook · Vertex AI job completion · upstream workflow completion · time schedule · data quality validation |
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cross_tool.deps |
Dataproc job trigger · BigQuery query completion · Dataflow pipeline status · Apache Airflow DAG trigger · Cloud Storage file validation · REST API call · downstream application delivery |
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cloud.platforms |
Google Cloud Platform · hybrid cloud · multi-cloud orchestration · Control-M SaaS · Control-M on-premises |
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error_handling |
configurable retry count · interval · downstream cascade prevention · automated workflow hold · SLA pre-breach alert · PagerDuty · Slack |
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throughput |
large-scale model training · high-volume batch inference · event-driven orchestration · parallel workflow execution · distributed data processing |
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observability |
job-level audit log · SLA tracking with breach prediction · dependency lineage graph · Datadog integration · Splunk integration · centralized workflow monitoring |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across Vertex AI, BigQuery, Dataflow, Dataproc, Airflow, Cloud Storage, 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 → BigQuery → Vertex AI training → model deployment
- Data-aware triggers: file arrival, Pub/Sub event, BigQuery completion, model validation result
|
Vertex AI |
training job orchestration · pipeline execution · deployment coordination · status monitoring |
|
BigQuery |
query execution · dependency management · data validation · completion tracking |
|
Dataflow |
pipeline triggering · status monitoring · SLA tracking · failure handling |
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Dataproc |
Spark workload orchestration · dependency control · automated recovery |
|
Cloud Storage |
file arrival detection · validation · event-based triggering |
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Apache Airflow |
DAG triggering · status tracking · cross-platform orchestration |
|
Google Cloud Services |
API orchestration · workflow automation · event 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 PIPELINES
Monitor Vertex AI workflows across the entire data ecosystem.
Vertex AI provides visibility into model activities, but production workflows span many platforms. Control-M delivers centralized monitoring across ingestion, preparation, training, deployment, and delivery processes in a single operational view:
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End-to-end workflow visibility
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Runtime history tracking
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Upstream dependency status
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Downstream delivery monitoring
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SLA risk indicators
SLA ASSURANCE
Keep model delivery commitments on schedule.
Machine learning workflows often involve multiple teams, tools, and dependencies. Control-M continuously evaluates workflow progress, predicts SLA risk, and initiates automated recovery actions before delays impact model consumers:
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Predictive SLA monitoring
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Automated escalation workflows
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Configurable recovery actions
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Dependency-aware scheduling
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Real-time status alerts
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
