- 12 Aug, 2025
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Integration Runtime Types in Azure Data Factory
Integration Runtime Types in Azure Data Factory – A Complete Guide
Azure Data Factory (ADF) is Microsoft’s fully managed, serverless data integration service that allows you to move and transform data from various sources in a secure and scalable way. At the heart of ADF’s architecture is the Integration Runtime (IR) — the compute infrastructure that enables these data movements and transformations.
What is Integration Runtime (IR)?
Integration Runtime is the engine that ADF uses to:
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Move data between sources and destinations.
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Execute transformations using Data Flows.
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Run SSIS (SQL Server Integration Services) packages in the cloud.
Think of IR as the bridge between your data sources and ADF pipelines — it determines how, where, and with what resources your data processing happens.
1 . Azure Integration Runtime (Azure IR)
The Azure Integration Runtime (Azure IR) is the default option in Azure Data Factory for running activities in a fully managed, serverless environment. It eliminates the need for any hardware or infrastructure setup, making it easy to start without maintenance overhead. Azure IR automatically scales based on workload demands, ensuring optimal performance for both small and large data operations. Security is handled through Azure-managed public endpoints, and you can select the region where data movement takes place to meet compliance and governance requirements. This type of IR is ideal for moving data between cloud-to-cloud sources—such as transferring data from Azure Blob Storage to Azure SQL Database—running Data Flows for complex transformations, or copying data between public cloud services without involving on-premises infrastructure. For example, you can seamlessly copy data from Amazon S3 to Azure Data Lake Storage entirely within the cloud.
2 . Self-Hosted Integration Runtime (Self-Hosted IR)
The Self-Hosted Integration Runtime (Self-Hosted IR) in Azure Data Factory is designed to access on-premises data sources or data within private networks that are not reachable via public endpoints. It is installed as an application on your own machine or virtual machine and can be configured as part of a high-availability cluster to ensure reliability. This runtime supports both data movement and Data Flow execution within private networks, making it ideal for secure, internal data transfers. Unlike Azure IR, it requires your team to handle monitoring, updates, and maintenance. Self-Hosted IR is best suited for scenarios such as copying data from an on-premises SQL Server to Azure Data Lake, transferring data between two firewalled databases, or enabling hybrid data integration that bridges cloud and private network environment.
3 . Azure-SSIS Integration Runtime (Azure-SSIS IR)
The Azure-SSIS Integration Runtime (Azure-SSIS IR) is a specialized runtime in Azure Data Factory designed to run SQL Server Integration Services (SSIS) packages directly in the cloud. It enables a seamless lift-and-shift approach, allowing you to migrate existing on-premises SSIS ETL packages to Azure without the need for rewriting or major modifications. This runtime can integrate with Azure SQL Managed Instance or Azure SQL Database and supports connectivity to both cloud-based and on-premises data sources. With a pay-as-you-go model, you are charged only for the hours the Azure-SSIS IR is running. It is ideal for scenarios such as migrating on-premises SSIS workloads to Azure, continuing to leverage SSIS for complex transformation logic, or running hybrid ETL processes that combine SSIS packages with other ADF activities.
Monitoring & Debugging in Azure Data Factory
Azure Data Factory (ADF) offers powerful tools to monitor and debug your data workflows, ensuring they run efficiently and reliably. The Monitor tab in the Azure portal lets you track pipeline runs, activity runs, and trigger runs, showing details like status, duration, and error messages. You can drill into activity runs to inspect input/output data and troubleshoot issues.
For testing, ADF’s debug mode allows you to run pipelines or Data Flows without publishing, preview data, and validate transformations. Integration with Azure Monitor and Log Analytics adds advanced alerting, logging, and diagnostics, helping you identify and fix problems quickly.
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