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Control-M + Dataiku
Your data pipeline starts before Dataiku executes—and it can fail when something upstream breaks. A file never arrived. A data pipeline missed its SLA. Control-M orchestrates the full chain: ingestion, preparation, Dataiku execution, 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 Dataiku workflows across multiple tools. Here’s how Control‑M handles each one.
UPSTREAM DATA
The 6:00 AM Dataiku flow started. Yesterday’s files never arrived.
Control-M monitors file arrivals across cloud storage, MFT platforms, and enterprise systems. It prevents Dataiku scenarios from launching until required data is present, validated, and complete, eliminating failed runs caused by missing inputs.
MODEL PIPELINES
Data preparation finished. The scoring workflow never triggered.
Control-M detects successful completion states across upstream jobs, evaluates dependencies, and automatically launches downstream Dataiku scenarios. End-to-end orchestration replaces manual handoffs, custom scripts, and fragile scheduler dependencies.
FAILURE RECOVERY
A Spark transformation failed at 2:17 AM. Nobody noticed
Control-M detects execution failures immediately, triggers configurable retries, routes alerts through PagerDuty or Slack, and prevents downstream Dataiku processes from consuming incomplete data or producing unreliable model outputs.
SLA RISK
The model refresh is running late. Business users need results by 8.
Control-M continuously tracks workflow progress against SLA targets, predicts breaches before deadlines are missed, and enables corrective action while there’s still time to protect downstream reporting and decision-making processes.
CROSS-PLATFORM DATAOPS
AWS, Databricks, Dataiku, and Snowflake all finished. Nobody trusts the status.
Control-M provides a single orchestration layer across platforms, tracking dependencies, execution status, and recovery actions from one place. Teams gain visibility into the entire workflow instead of isolated tool-level views.
INTEGRATION FACTS
Control‑M + Dataiku
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workload.types |
Dataiku jobs · Dataiku scenarios · dataset rule computation · data preparation pipelines · machine learning model runs · batch analytics workloads |
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trigger.type |
file arrival (S3 · Azure Blob · GCS · SFTP) · API/webhook · upstream job completion · Dataiku scenario completion · event-based trigger · time schedule |
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cross_tool.deps |
Apache Airflow DAG trigger · Databricks job completion · Snowflake workload execution · Spark processing · Fivetran sync completion · REST API call · file delivery confirmation |
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cloud.platforms |
AWS · Microsoft Azure · Google Cloud Platform · hybrid environments · Control-M SaaS + 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 |
high-volume batch processing · machine learning pipelines · large-scale data preparation · parallel workflow execution · event-driven orchestration |
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observability |
job-level audit log · SLA tracking with breach prediction · dependency lineage graph · Datadog/Splunk integration · SIEM-compatible event stream |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across Dataiku, Snowflake, Databricks, Spark, 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: file arrival → Dataiku preparation → model training → Snowflake publish → BI delivery
- Data-aware triggers: file arrival, API event, upstream job completion, Dataiku scenario exit-state detection
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Dataiku |
scenario execution · workflow orchestration · status tracking · dependency management |
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Snowflake |
warehouse execution · SQL processing · downstream publishing |
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Databricks |
job triggering · status monitoring · recovery coordination |
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Apache Spark |
transformation orchestration · dependency control · execution tracking |
|
Apache Airflow |
DAG triggering · status collection · workflow coordination |
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Cloud Storage (S3/Azure Blob/GCS |
file detection · event triggers · data readiness validation |
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BI Platforms |
report delivery · analytics refresh · completion confirmation |
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 issues 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 a data pipelines
- Python operators, sensors, and task dependencies
- Execution graphic 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 ever starts
- Existing DAGs don’t need to be rewritten or migrated
MONITOR WORKFLOWS
Monitor Dataiku pipelines across every dependent platform.
Dataiku provides visibility into its own processes, but production workflows extend beyond a single platform.
Control-M provides centralized monitoring across ingestion, transformation, machine learning, and delivery processes in a single operational view:
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Pipeline execution status
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Runtime history tracking
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Upstream dependencies
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Downstream dependencies
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SLA risk indicators
sla assurance
Keep Dataiku deliverables on schedule.
Data science and analytics teams depend on predictable delivery windows, but Dataiku cannot manage every upstream dependency.
Control-M tracks complete workflow execution, predicts SLA risks, and automates recovery actions before deadlines are missed:
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SLA breach prediction
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Automated escalation
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Configurable recovery actions
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Dependency-aware scheduling
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Business deadline tracking
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
