I have created a CI-CD pipeline to deploy pipelines from my development data factory to production data factory and it works perfect for the first run. However, when a change is made in the dev, for example,
renamed pipeline1 to pipeline001
the production data factory has a pipeline by the name pipeline001 and also pipeline1, both of which are the same. The issues persists for any kind of change and not just restricted to renaming.
I checked the json templates, which was found ok.
Any help?
Related
can any one help me how to lock pipeline in ADF, is there any option when one developer is working other should not work, as with multiple developers are working on same pipeline without using Source Control
unfortunately there is no feature in Azure portal for Azure data factory to lock the pipeline changes if 2 or more are working on the same pipeline. You would have to create a clone of existing pipeline and work on those clones else the best way is to use source control like git
I've been reading the following link.
https://learn.microsoft.com/en-us/azure/data-factory/continuous-integration-delivery-improvements
It mentions using npm to build the Data Factory ARM templates and use the resulting artefact to deploy to UAT/Prod etc instead of using the adf_publish branch.
Has anyone got a sample Yaml file that does this?
Also, how would you handle overriding the ARM Template Parameters Json file for changing over parameterization such as the Environments Key Vault etc e.g. Dev-KV -> UAT-KV, Prod-KV
This is what I did. I followed this article to get my yaml file setup, there is a github repo in the article that has all of this persons code.
Azure Data Factory CI-CD made simple: Building and deploying ARM templates with Azure DevOps YAML Pipelines
Then I used Global parameters and referenced those everywhere in my pipelines. Here is a ref for that: Global parameters in Azure Data Factory
And finally, I used the overrideParameters option in my yaml pipeline to deploy the correct version of the parameter to the correct environment. Here is a ref for that: ADF Release - Set global params during deployment
Hope that helps!
I want to automate Azure data factory pipeline deployments.
I have Self Hosted Integration runtimes with a different name in each environment (i.e. SHIR-{environment}).
I have different data sources and destinations for each environment. (i.e. different SQL server names or Hostnames)
How can I perform the automatic weekly deployments to promote changes from GitHub dev branch to stage and stage to production? I don't want to modify these database server names in linked services during the GitHub PR merge.
To set up automated deployment, start with an automation tool, such as Azure DevOps. Azure DevOps provides various interfaces and tools in order to automate the entire process.
A development data factory is created and configured with Azure Repos Git. All developers should have permission to author Data Factory resources like pipelines and datasets.
A developer creates a feature branch to make a change. They debug their pipeline runs with their most recent changes. For more information on how to debug a pipeline run, see Iterative development and debugging with Azure Data Factory.
After a developer is satisfied with their changes, they create a pull request from their feature branch to the main or collaboration branch to get their changes reviewed by peers.
After a pull request is approved and changes are merged in the main branch, the changes get published to the development factory.
When the team is ready to deploy the changes to a test or UAT (User Acceptance Testing) factory, the team goes to their Azure Pipelines release and deploys the desired version of the development factory to UAT. This deployment takes place as part of an Azure Pipelines task and uses Resource Manager template parameters to apply the appropriate configuration.
After the changes have been verified in the test factory, deploy to the production factory by using the next task of the pipelines release.
For more information follow this link
Is there a way to unit test individual pipelines in Azure Data Factory on a particular branch without having to deploy my changes. Currently the only way I am able to run unit tests on ADF pipelines is by publishing my changes to the data factory instance and kick off a pipeline run. However this approach requires me to merge my changes to the collaboration branch before I am able to execute any pipeline test cases.
Ideally I'd like to be able to kick off a pipeline on particular feature branch without having to deploy to the default instance, so that I can validate my test case and make adjustments before merging it with the collaboration branch.
Any suggestions people can give or resources they can point to?
I think you can try going for Automated Unit Testing for ADF, this will enable you to write a code and the test runs before deployment.
You can check a sample for the same here
At the time of writing it is not possible to command the Azure Data Factory API to run pipes in debug mode. You can call the published pipes through the REST API. If you do not want to modify the published configuration of Azure Data Factory you would need to have multiple data factories.
We use 1 ADF per engineer, and 1 ADF per higher level environment.
I need to know how can i build continuous deployment for Azure Data factory using VSTS. I know there is an Azure data factory deployment available in VSTS release. But I'm looking for other options using Powershell for deployment.
If anyone has already done anything specific to this provide the links.
This blog should get you started. I'm using a comparable method for deployment. Before deploying the JSON files using a PowerShell command, I edit them to insert environment specific values into the Data Factory definitions. You can pass these values as parameters from the TFS deployment-pipeline.