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Control-M + GCP BigQuery
Your data pipeline starts before BigQuery executes a query — and it can fail when something upstream breaks. A file lands late. A Dataflow job overruns. Control-M orchestrates the full chain: ingestion, transformation, BigQuery execution, validation, and downstream delivery.
common workflow issues
Does this sound like your week?
These aren’t edge cases. They’re the normal operating conditions for teams running BigQuery workloads across multiple tools. Here’s how Control‑M handles each one.
DATA ARRIVAL
The 6:00 AM dashboard failed because yesterday’s files arrived at 6:07.
Control-M waits for actual file arrival events instead of fixed schedules. It validates delivery, checks file integrity, and releases downstream BigQuery jobs only when prerequisites are met, eliminating premature execution and failed reporting cycles.
PIPELINE DEPENDENCIES
Dataflow finished. BigQuery never started. Nobody noticed until users called.
Control-M detects Dataflow completion states, evaluates dependency conditions, and automatically launches BigQuery workloads. Status is tracked end-to-end, preventing silent failures between connected platforms and reducing operational overhead.
SLA RISK
Your finance report is due at 8:00. The transformation job is still running.
Control-M continuously tracks execution against SLA targets, predicts potential breaches before they occur, and triggers alerts or remediation workflows. Teams gain time to act before business-critical reporting deadlines are missed.
FAILURE RECOVERY
One upstream API timed out. Twenty downstream jobs failed with it.
Control-M applies configurable retry policies, prevents unnecessary downstream execution, and isolates failures before they cascade. Recovery actions can be automated, reducing manual intervention and shortening time to resolution.
CROSS-CLOUD DATA
AWS data landed late. BigQuery processed incomplete datasets.
Control-M orchestrates workflows across cloud platforms, storage services, and data tools. Cross-platform dependencies are validated before execution, ensuring BigQuery workloads run against complete and trusted datasets.
INTEGRATION FACTS
Control‑M + GCP BigQuery
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workload.types |
SQL query execution · stored procedure execution · table function execution · data load from Google Cloud Storage · data export to Google Cloud Storage · data copy between tables · ELT pipeline execution |
|
trigger.type |
file arrival (Cloud Storage · SFTP · object storage) · API/webhook · Dataflow completion · Airflow DAG completion · upstream job exit code · time schedule |
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cross_tool.deps |
Apache Airflow DAG trigger · Google Dataflow completion · Dataproc job execution · dbt run completion · Fivetran sync completion · Spark workload status · REST API integration |
|
cloud.platforms |
Google Cloud Platform · AWS · Microsoft Azure · hybrid cloud environments · Control-M SaaS · on-premises Control-M |
|
error_handling |
configurable retry count · automated rerun · downstream cascade prevention · job hold on upstream failure · SLA pre-breach alert · PagerDuty · Slack |
|
throughput |
large-scale batch processing · high-volume analytics workloads · parallel job orchestration · up to 50 concurrent BigQuery jobs per Agent |
|
observability |
job-level audit log · dependency lineage graph · SLA tracking with breach prediction · Datadog integration · centralized workflow monitoring |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across BigQuery, Dataflow, Airflow, Dataproc, dbt, 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: Data ingestion → Dataflow → BigQuery → dbt → BI publishing
- Data-aware triggers: file arrival, API event, Dataflow completion, query result
|
Google BigQuery |
query execution orchestration · workload scheduling · dependency management · status monitoring |
|
Google Cloud Storage |
file arrival detection · data validation · ingestion triggering · event-based automation |
|
Google Dataflow |
completion detection · pipeline coordination · dependency tracking · automated workflow execution |
|
Apache Airflow |
DAG triggering · status synchronization · SLA coordination · cross-platform orchestration |
|
Google Dataproc |
Spark job orchestration · cluster workload management · execution monitoring · recovery automation |
|
dbt Cloud |
transformation workflow triggering · run status tracking · dependency management · downstream job release |
|
Tableau |
data source refresh orchestration · workbook publishing · extract refresh scheduling · job completion monitoring |
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 BigQuery execution across your entire data ecosystem.
BigQuery shows query execution, but operational teams need visibility across every dependency feeding and consuming warehouse workloads. Control-M provides centralized monitoring, execution tracking, and workflow visibility across the complete pipeline:
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Query execution status
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Runtime history tracking
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Dependency visibility
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Workflow health monitoring
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Centralized operational dashboard
SLA ASSURANCE
Keep BigQuery data products on schedule.
BigQuery doesn't manage business SLAs across upstream and downstream systems. Control-M continuously evaluates workflow progress, predicts deadline risks, and automates response actions before delivery commitments are missed:
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SLA breach prediction
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Automated escalation workflows
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Deadline-aware scheduling
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Proactive alerting
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Business service tracking
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
