![]() Unfortunately, I found this way of little use and hard to use. The whole concept and details are described here: Continuous integration and delivery in Azure Data Factory. I knew that Microsoft recommends only one way of CI/CD for Data Factory. In most cases, we used an approach of deploying ADF directly from code, from a branch in GIT repository. I used ADF in a few organisations, so far, for the last few years. Until you try to promote and deploy the code of ADF to further environment (or stage – using naming nomenclature from Azure DevOps). Also, as long as you develop the solution only in one instance (let’s name it DEV environment), you will not spot any issue or problem. ![]() While working with ADF UI by the browser may not be a preferable way for many (there is no Visual Studio support for ADFv2 as it was for v1 and Microsoft is not planning to do so in nearest time), the UI is better and better and doesn’t cause much trouble when building pipelines. #Azure data studio powershell 6 notebook how toWe are focusing here on governance the code of ADF and how to deploy it from a code repository to the service, hence mentioning the integration with GIT was on purpose. ![]() Today, ADF is a pretty mature product and offers a lot of useful features, including Global Parameters, Mapping Data Flow, GIT integration and much more. At the time, the whole ADFv2 was in preview and the product was (and still is) fastly develop by MS Team (kudos to Mark Kromer and his team). This post is completely focused on the topic of deployment (aka publish) Azure Data Factory from code to the instance (service) in the cloud. This post is NOT about what Azure Data Factory is, neither how to use, build and manage pipelines, datasets, linked services and other objects in ADF. Two methods of deployment Azure Data FactoryĪzure Data Factory is a fantastic tool which allows you to orchestrate ETL/ELT processes at scale. ![]()
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