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Control-M + Matillion
Your Matillion workflow starts before an orchestration pipeline ever executes — and it can fail when something upstream breaks. An Amazon S3 object arrives late. A Fivetran sync misses its scheduled completion. Control-M orchestrates the full chain: source data arrival, upstream ingestion, Matillion execution, and downstream warehouse delivery.
common workflow issues
Does this sound like your week?
These aren’t edge cases. They’re the normal operating conditions for teams running Matillion pipelines across multiple tools. Here’s how Control-M handles each one.
LATE DATA ARRIVAL
Your 2 a.m. S3 data is late. Matillion is already scheduled.
Control-M makes Matillion execution dependent on the required upstream data instead of an isolated clock. The pipeline starts only after its prerequisite completes, preventing incomplete data from moving into transformation and downstream warehouse workloads.
PIPELINE FAILURE
Matillion started successfully. Fifteen minutes later, the pipeline fails.
Control-M polls Matillion execution status at a configurable frequency and applies failure tolerance before marking the job Not OK. Downstream dependencies remain controlled by the workflow, preventing a failed Matillion execution from silently propagating bad outcomes.
CROSS-TOOL DEPENDENCY
Fivetran finishes late. Your Matillion pipeline is still waiting.
Control-M coordinates the upstream job and Matillion execution as dependencies in one scheduling environment. Successful completion releases the next stage automatically, replacing disconnected schedules and manual handoffs with an explicit production workflow.
STATUS CHECKS
The pipeline is running. Your team keeps checking Matillion for completion.
Control-M tracks Matillion pipeline status using configurable status polling. Completion becomes part of the wider Control-M workflow state, allowing dependent processing to continue without operators repeatedly checking the Matillion console.
CLOUD · ON-PREMISES
Matillion finishes in cloud. An on-premises process needs the result.
Control-M coordinates Matillion with other jobs in the enterprise workflow, allowing completion to govern what runs next across platform boundaries. Data teams manage the dependency as one production flow instead of stitching together separate schedulers and handoffs.
INTEGRATION FACTS
Control‑M + Matillion
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workload.types |
Data Productivity Cloud pipelines · Matillion ETL pipelines · ETL workflows · data transfer · transformation pipelines · warehouse loading |
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trigger.type |
time schedule · upstream job completion · file arrival · Control-M condition · API-driven execution · cross-tool dependency |
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cross_tool.deps |
Fivetran sync completion · Airflow DAG · Snowflake job · Databricks job · file transfer · REST API call · downstream analytics job |
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cloud.platforms |
Matillion Data Productivity Cloud · Matillion ETL on VM · AWS · Microsoft Azure · Google Cloud Platform |
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error_handling |
configurable status polling · failure tolerance · downstream dependency control · job Not OK state · Control-M recovery actions · SLA management |
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throughput |
scheduled batch processing · parallel workflow execution · resource-managed workloads · cross-platform ETL orchestration |
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observability |
job status monitoring · execution results · job output · end-to-end workflow status · dependency visibility · SLA tracking |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across Matillion, Fivetran, Snowflake, 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: Fivetran → Matillion pipeline → Snowflake → analytics handoff
- Data-aware triggers: file arrival, API event, upstream completion, pipeline result
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Matillion |
launch pipelines · launch orchestration jobs · poll status · apply failure tolerance |
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Fivetran |
trigger syncs · track completion · coordinate downstream processing |
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Snowflake |
execute jobs · coordinate dependencies · manage downstream workloads |
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Airflow |
trigger DAGs · track execution · coordinate DAG dependencies |
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Databricks |
execute jobs · track completion · coordinate processing |
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File transfers |
detect arrival · transfer files · release dependent processing |
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Cloud services |
coordinate cloud jobs · connect cross-platform dependencies |
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 Matillion execution across your complete data workflow.
Matillion provides execution visibility for its own pipelines, but production data flows often span multiple platforms. Control-M brings Matillion execution into the broader workflow so teams can monitor processing and dependencies from one operational view:
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Matillion execution status
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Job results and output
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Upstream and downstream dependencies
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Cross-platform workflow visibility
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End-to-end execution context
SLA ASSURANCE
Keep Matillion pipelines aligned with downstream SLAs.
A successful Matillion run can still deliver data too late for the business process it supports. Control-M places Matillion inside the end-to-end service, connecting execution status and dependencies with the SLA that matters:
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End-to-end SLA tracking
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Matillion job status monitoring
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Dependency-aware workflow control
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Downstream cascade prevention
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Centralized operational visibility
Documentation
Bring order to complex workflows
Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.
