Since 2005, SQL Server Integration Services or SSIS, has provided many organizations with a platform for developing extract, transformation, and load or ATL solutions, for On-premises data warehouses, analytical solutions, and in some cases, migration projects. Years of investment in understanding an organization's, data and the development effort to create data integration pipelines, must be considered when moving to a cloud data integration solution. As your data factory provides the capability to lift and shift SSIS solutions to the cloud, so that they can be utilized immediately. In this lesson, you will learn how to use Azure Data Factory to transfer SSIS solutions to the cloud. After completing this lesson, you will be able to describe SQL Server Integration Services, explain the Azure SSIS integration runtime, set up the Azure SSIS integration runtime, run SSiS package in Azure, and migrate SSIS packages to Azure. Before taking this lesson, it is recommended that you can log into the Azure portal, explain and create resource groups, describe Azure Data Factory and its core components, and ingest data into Azure Data Factory using the Copy Activity. SQL Server Integration Services, or SSIS, is a platform for building complex, extract, transform, and load, or ETL Solutions. SSIS is a component within SQL Server and consists of a Windows service that manages the execution of ETL workflows, along with several tools and components for developing those workflows. SSIS is typically used to develop data integration pipelines for On-premises data warehousing solutions. It can also be used to create data migration pipelines when migrating data between different systems. SSIS Is primarily a control flow engine that manages the execution of workflows. Workflows are held in packages, which can be executed on demand, or on a schedule. In the development of SSIS packages, the task workflow is referred to as the control flow of the package. A control flow includes one or more tasks, which manage operations such as data flows. SSIS executes these tasks by using a data flow engine that encapsulates the data flow in a pipeline. Each series of data flow tasks operates in sequence, processing each row set of data in turn, as it passes through the pipeline. A SSIS solution usually consists of one or more SSIS projects, each containing one or more SSIS packages. So, let's take a look now at SSIS projects and packages. From SQL Server 2012, a project is the unit of deployment for SSIS Solutions. You can define project level parameters to enable users to specify runtime settings, and project level connection managers that reference data sources and destinations used in package data flows. You can then deploy projects to an SSIS catalog in a SQL Server instance, and configure project level parameter values and connections as appropriate for execution environments. An SSIS project contains one or more SSIS packages, each package defines a workflow of tasks to be executed. The workflow of tasks in a SSIS package is referred to as its control flow. A package control flow can include one or more data flow task, each of which encapsulates its own data flow pipeline. Packages can include package level parameters, so that dynamic values can be passed to the package at runtime. In previous releases of SSIS, deployment was managed at the package level.