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The Integration Runtime (IR) is the compute infrastructure used by Azure Data Factory and Azure Synapse pipelines to provide the following data integration capabilities across different network environments:
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In Data Factory and Synapse pipelines, an activity defines the action to be performed. A linked service defines a target data store or a compute service. An integration runtime provides the bridge between activities and linked services. It's referenced by the linked service or activity, and provides the compute environment where the activity is either run directly or dispatched. This allows the activity to be performed in the closest possible region to the target data store or compute service to maximize performance while also allowing flexibility to meet security and compliance requirements.
Data Factory offers three types of Integration Runtime (IR), and you should choose the type that best serves your data integration capabilities and network environment requirements. The three types of IR are:
Outbound controls vary by service for Azure IR. In Synapse, workspaces have options to limit outbound traffic from the managed virtual network when utilizing Azure IR. In Data Factory, all ports are opened for outbound communications when utilizing Azure IR. Azure-SSIS IR can be integrated with your vNET to provide outbound communications controls.
Azure Integration Runtime supports connecting to data stores and computes services with public accessible endpoints. Enabling Managed Virtual Network, Azure Integration Runtime supports connecting to data stores using private link service in private network environment. In Synapse, workspaces have options to limit outbound traffic from the IR managed virtual network. In Data Factory, all ports are opened for outbound communications. The Azure-SSIS IR can be integrated with your vNET to provide outbound communications controls.
Azure integration runtime provides a fully managed, serverless compute in Azure. You don't have to worry about infrastructure provision, software installation, patching, or capacity scaling. In addition, you only pay for the duration of the actual utilization.
Azure integration runtime provides the native compute to move data between cloud data stores in a secure, reliable, and high-performance manner. You can set how many data integration units to use on the copy activity, and the compute size of the Azure IR is elastically scaled up accordingly without requiring you to explicitly adjust the size of the Azure Integration Runtime.
If you want to perform data integration securely in a private network environment that doesn't have a direct line-of-sight from the public cloud environment, you can install a self-hosted IR in your on-premises environment behind a firewall, or inside a virtual private network. The self-hosted integration runtime only makes outbound HTTP-based connections to the internet.
Install a Self-hosted IR on an on-premises machine or a virtual machine inside a private network. Currently, the self-hosted IR is only supported on a Windows operating system.
For high availability and scalability, you can scale out the self-hosted IR by associating the logical instance with multiple on-premises machines in active-active mode. For more information, see the article on how to create and configure a self-hosted IR for details.
The Azure-SSIS IR can be provisioned in either public network or private network. On-premises data access is supported by joining Azure-SSIS IR to a virtual network that is connected to your on-premises network.
The Azure-SSIS IR is a fully managed cluster of Azure VMs dedicated to run your SSIS packages. You can bring your own Azure SQL Database or SQL Managed Instance for the catalog of SSIS projects/packages (SSISDB). You can scale up the power of the compute by specifying node size and scale it out by specifying the number of nodes in the cluster. You can manage the cost of running your Azure-SSIS Integration Runtime by stopping and starting it as your requirements demand.
For more information, see How to create and configure the Azure-SSIS IR. Once created, you can deploy and manage your existing SSIS packages with little to no change using familiar tools such as SQL Server Data Tools (SSDT) and SQL Server Management Studio (SSMS), just like using SSIS on-premises.
When you create an instance of Data Factory or a Synapse Workspace, you need to specify its location. The metadata for the instance is stored here, and triggering of the pipeline is initiated from here. Metadata is only stored in the chosen region and will not be stored in other regions.
Meanwhile, a pipeline can access data stores and compute services in other Azure regions to move data between data stores or process data using compute services. This behavior is realized through the globally available IR to ensure data compliance, efficiency, and reduced network egress costs.
The IR Location defines the location of its back-end compute, and where the data movement, activity dispatching, and SSIS package execution are performed. The IR location can be different from the location of the Data Factory it belongs to.
For copy activity, a best effort is made to automatically detect your sink data store's location, then use the IR in either the same region, if available, or the closest one in the same geography, otherwise; if the sink data store's region is not detectable, the IR in the instance's region is used instead.
If you have strict data compliance requirements and need to ensure that data do not leave a certain geography, you can explicitly create an Azure IR in a certain region and point the Linked Service to this IR using the ConnectVia property. For example, if you want to copy data from a blob in UK South to an Azure Synapse workspace in UK South and want to ensure data does not leave the UK, create an Azure IR in UK South and link both Linked Services to this IR.
For Lookup/GetMetadata/Delete activity execution (Pipeline activities), transformation activity dispatching (External activities), and authoring operations (test connection, browse folder list and table list, and preview data), the IR in the same region as the Data Factory or Synapse Workspace is used.
A best practice is to ensure data flows run in the same region as your corresponding data stores when possible. You can either achieve this with auto-resolve for the Azure IR (if the data store location is the same as the Data Factory or Synapse Workspace location), or by creating a new Azure IR instance in the same region as your data stores and then executing the data flows on it.
The self-hosted IR is logically registered to the Data Factory or Synapse Workspace and the compute used to support its functionalities is provided by you. Therefore there is no explicit location property for self-hosted IR.
If an activity associates with more than one type of integration runtime, it will resolve to one of them. The self-hosted integration runtime takes precedence over the Azure integration runtime in Azure Data Factory or Synapse Workspace instances using a managed virtual network. And the latter takes precedence over the global Azure integration runtime.
For example, one copy activity is used to copy data from source to sink. The global Azure integration runtime is associated with the linked service to source and an Azure integration runtime in an Azure Data Factory managed virtual network associates with the linked service for sink, then the result is that both source and sink linked services use the Azure integration runtime in the Azure Data Factory managed virtual network. But if a self-hosted integration runtime associates the linked service for source, then both source and sink linked service use the self-hosted integration runtime.
The Copy activity requires both source and sink linked services to define the direction of data flow. The following logic is used to determine which integration runtime instance is used to perform the copy:
Each external transformation activity that utilizes an external compute engine has a target compute linked service, which points to an integration runtime. This IR instance determines the location from where that external hand-coded transformation activity is dispatched.
Data Flow activities are executed on their associated Azure integration runtime. The Spark compute utilized by Data Flows are determined by the data flow properties in your Azure IR, and are fully managed by the service.
Integration runtimes don't change often and are similar across all stages in your CI/CD. Data Factory requires you to have the same name and type of integration runtime across all stages of CI/CD. If you want to share integration runtimes across all stages, consider using a dedicated factory just to contain the shared integration runtimes. You can then use this shared factory in all of your environments as a linked integration runtime type.
The integration runtime (IR) is the compute infrastructure that Azure Data Factory and Synapse pipelines use to provide data-integration capabilities across different network environments. For details about IR, see Integration runtime overview.
A self-hosted integration runtime can run copy activities between a cloud data store and a data store in a private network. It also can dispatch transform activities against compute resources in an on-premises network or an Azure virtual network. The installation of a self-hosted integration runtime needs an on-premises machine or a virtual machine inside a private network. 152ee80cbc
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