Let us know how we can help
Sales & Pricing
Speak to a rep about your business needs
Help & Support
See our product support options
General inquiries and locations
Contact us
Control-M + Snowflake Cortex AI
Your Snowflake Cortex AI workflow starts before a Cortex function call ever runs against a prepared dataset — and it can fail when something upstream breaks. A Snowpipe ingestion batch never completes its load into the target Snowflake table. A dbt Cloud job overruns its scheduled run window before writing its transformed feature table to Snowflake. Control-M orchestrates the full chain: Snowpipe ingestion, dbt Cloud transformation, Snowflake Cortex AI execution, and Tableau delivery.
common workflow issues
Does this sound like your week?
MODEL READINESS
Training data landed late. The Cortex AI job ran anyway.
Control-M validates upstream dependencies before execution. It confirms ingestion completion, data quality checks, and transformation status before triggering Cortex AI workloads, preventing failed predictions and wasted compute cycles.
CROSS-TOOL DEPENDENCIES
dbt completed at 2:07 AM. AI inference never started.
Control-M detects dbt completion events, evaluates workflow conditions, and launches Cortex AI processing automatically. No custom polling scripts, manual intervention, or disconnected schedulers are required.
SLA RISK
Executive forecasts were due by 7 AM. Results arrived at 8:15.
Control-M continuously tracks workflow progress, predicts SLA breaches before they occur, and alerts operators early enough to take corrective action before business deadlines are missed.
FAILURE RECOVERY
One upstream API timed out. The pipeline kept failing.
Control-M applies configurable retry policies, error handling logic, and dependency-aware recovery. Failed components are isolated while preventing unnecessary downstream execution and cascading workflow failures.
AI OPERATIONS
The model finished. Nobody knew where results went.
Control-M orchestrates post-processing activities including result validation, table updates, file delivery, BI refreshes, and notification workflows, ensuring outputs reach downstream consumers automatically.
INTEGRATION FACTS
Control‑M + Snowflake Cortex AI
|
workload.types |
AI agent workflow execution · Snowflake Cortex AI Agent orchestration · MCP Server tool call execution · generative AI pipeline orchestration · agentic workflow automation |
|
trigger.type |
file arrival (S3 · Azure Blob · GCS) · API/webhook · dbt completion · Snowflake task completion · time schedule · upstream workflow status |
|
cross_tool.deps |
dbt Cloud run trigger · Apache Airflow DAG trigger · Fivetran sync completion · Databricks job completion · Spark workload status · REST API orchestration · BI refresh workflows |
|
cloud.platforms |
AWS · Microsoft Azure · Google Cloud Platform · Snowflake Native Platform · Control-M SaaS · on-premises |
|
error_handling |
configurable retry count · dependency-aware recovery · downstream cascade prevention · automated workflow hold · SLA breach prediction · PagerDuty · Slack |
|
throughput |
high-volume batch processing · large-scale AI inference · parallel workflow execution · event-driven orchestration · enterprise-scale data pipelines |
|
observability |
job-level audit log · SLA tracking · dependency lineage visualization · Datadog integration · Splunk integration · centralized operational dashboard |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across Snowflake Cortex AI, dbt, Airflow, Fivetran, 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: Fivetran → dbt → Snowflake Cortex AI → Tableau refresh
- Data-aware triggers: file arrival, API event, dbt completion, Cortex AI result generation
|
Snowflake Cortex AI |
workflow orchestration · AI workload execution · dependency management · status monitoring |
|
dbt Cloud |
run triggering · completion detection · dependency coordination |
|
Apache Airflow |
DAG execution · status tracking · workflow synchronization |
|
Fivetran |
sync completion detection · ingestion coordination · dependency enforcement |
|
Databricks |
Spark job orchestration · status monitoring · cross-platform workflows |
|
Tableau |
dashboard refresh automation · delivery scheduling · report distribution |
|
Cloud Storage (S3/Azure/GCS) |
file arrival triggers · data readiness validation · event-driven execution |
MONITOR AI WORKFLOWS
Monitor Snowflake Cortex AI execution end-to-end.
Snowflake Cortex AI provides model execution capabilities, but operational visibility often spans multiple platforms.
Control-M delivers a centralized view across ingestion, preparation, AI processing, and delivery workflows:
-
Workflow execution status
-
Runtime history tracking
-
Cross-platform dependencies
-
AI pipeline visibility
-
Centralized operational dashboards
sla assurance
Keep AI-powered data products on schedule.
AI workflows frequently depend on multiple upstream systems and strict business deadlines.
Control-M tracks dependencies, predicts SLA risks, and automates remediation before missed deadlines impact consumers:
-
SLA breach prediction
-
Dependency-aware scheduling
-
Automated recovery actions
-
Proactive operational alerts
-
Business deadline tracking
