Control-M + SQL Server Integration Services (SSIS)

Your SSIS workflow starts before an SSIS package ever executes — and it can fail when something upstream breaks. An Azure Blob Storage file arrives late. A SQL Server Agent job misses its processing window. Control-M orchestrates the full chain: source data arrival, database preparation, SSIS package execution, and downstream delivery.

See it on a pipeline like yours View integration facts

common workflow issues

Does this sound like your week?

These aren’t edge cases. They’re the normal operating conditions for teams running SSIS packages across multiple tools. Here’s how Control-M handles each one.

DATA ARRIVAL

Your 2 a.m. SSIS package started. The Azure Blob file didn’t arrive.

Control-M coordinates file arrival with package execution, so the SSIS workload waits for its required upstream dependency instead of running against missing data. Downstream processing remains gated until the prerequisite completes successfully.

PACKAGE FAILURE

The SSIS package failed overnight. Downstream reporting is already queued.

Control-M detects the failed job state, applies defined recovery actions, and prevents dependent jobs from proceeding prematurely. Operations teams see the failure in the context of the complete workflow instead of discovering broken reports downstream.

CROSS-TOOL DEPENDENCY

SQL processing finished. Your SSIS package is still waiting for its schedule.

Control-M models SQL processing and SSIS execution in the same workflow, using completion status rather than disconnected schedules to control progression. The package starts when its actual prerequisite succeeds, eliminating unnecessary timing gaps.

ENVIRONMENT COORDINATION

The package is ready. The wrong SSIS environment breaks production execution.

Control-M SSIS jobs can specify the SSIS Catalog environment used for package execution, along with configuration files and package properties. Teams can operationalize the correct execution context while keeping orchestration centrally controlled.

SLA RISK

SSIS completed late. Your 7 a.m. analytics handoff is now at risk.

Control-M tracks the SSIS job within the end-to-end workflow and applies SLA management across dependent processing. Teams can identify timing risk before the final delivery misses its business deadline and focus recovery on the affected chain.

Control‑M + SQL Server Integration Services (SSIS)

workload.types	SSIS packages (from SSIS Catalog, SSIS Package Store, SQL Server, or File System sources) · SQL scripts · stored procedures
trigger.type	file arrival · upstream job completion · time schedule · SQL job completion · workflow dependency
cross_tool.deps	SQL Server job · Azure Blob file transfer · database query · Apache Airflow DAG · downstream analytics handoff
cloud.platforms	Microsoft Azure · Azure SQL Database · SQL Server · hybrid environments · on-premises infrastructure
error_handling	configurable retries · connection retries · downstream cascade prevention · dependency conditions · SLA monitoring · failure notifications · recovery actions
throughput	enterprise batch pipelines · concurrent database connections · scheduled ETL processing · multi-package workflows · high-volume data processing
observability	centralized job monitoring · workflow status · dependency visibility · SLA tracking · execution history · failure alerts · audit visibility

end-to-end orchestration

One production workflow. Every tool in the stack.

Control-M orchestrates workflows across SQL Server Integration Services, SQL Server, Azure Blob Storage, file transfers, and analytics services in a single job flow — and coordinates alongside Apache Airflow — with dependency tracking, SLA visibility, and automated recovery across all of them.

Cross-tool dependency: Azure Blob Storage → SQL Server → SSIS package → analytics handoff
Data-aware triggers: file arrival, API event, SQL job completion, package completion

View all integrations

SQL Server Integration Services	package execution · SSIS Catalog environment · configuration files · package properties · status monitoring
SQL Server	SQL scripts · stored procedures · SQL Server Agent jobs · dependency coordination
Azure Blob Storage	file arrival dependency · upstream data handoff · workflow triggering
Apache Airflow	DAG coordination · cross-tool dependencies · upstream/downstream orchestration
Managed File Transfer	secure file movement · arrival detection · delivery confirmation
Analytics services	downstream triggering · dependency gating · SLA 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 SSIS execution across the complete data pipeline

SSIS logging (such as the SSISDB catalog) records detailed package executions, but production pipelines rarely stop at SSIS. Control-M gives data teams centralized operational visibility across SSIS packages and the upstream and downstream jobs that determine whether delivery succeeds:

SSIS package execution status
Upstream and downstream dependencies
End-to-end workflow visibility
Centralized failure monitoring
Execution and runtime history

SLA ASSURANCE

Keep SSIS pipelines aligned with business deadlines

A successful SSIS package can still produce a late business outcome when upstream data arrives late or downstream processing stalls. Control-M manages the package within its complete service workflow so teams can protect delivery commitments:

End-to-end SLA tracking
Dependency-aware execution
SLA risk visibility
Automated recovery actions
Downstream cascade prevention

Documentation

Integrating SQL Server Integration Services (SSIS) and Control-M

Bring order to complex workflows

Learn how Control-M helps teams orchestrate complex processes with greater visibility, coordination, and control.

Schedule a consultation Start a self guided demo

Workflow Orchestration

Mainframe Simplification