As we want to work with the adf_publish branch, we need our YAML config file in that branch.Īzure DevOps automatically picks up the default branch when selecting where to store the YAML config file.
FILR FACTORY CODE
The code stored in the main/master branch will never be promoted to where the ARM templates are output, in the adf_publish branch and vice versa.
![filr factory filr factory](https://dwgshare.com/wp-content/uploads/2022/08/25.Factory-Layout-and-perspectives-Free-Drawing-2.jpg)
There is a catch here though, which is specific to how Azure Data Factory uses git branches:Īzure Data Factory does not follow a standard git flow. With our support script in place and data factory ready to be published we can now create our YAML pipeline.
![filr factory filr factory](https://windows-cdn.softpedia.com/screenshots/DTM-File-Factory-Standard_15.png)
Create new file in git repo Create the pipeline Paste the script from Microsoft’s docs page in and hit Commit. Set the name as adf-maintenance.ps1 (prefixed with a subfolder if you wish). If you’re not overly familiar with git, you can check the Add a README box to initialize the repo and then select New > File from the three dots in the top right.
FILR FACTORY HOW TO
It also allows me to demonstrate how to pull from multiple repos in the YAML file. This is an optional step and the script file can sit inside the same repo as your Data Factory ARM templates but I’ve separated it out to another repo for better visibility and so I can add other config scripts later. We’ll start with creating a new “configs” git repository and committing Microsoft’s “stop trigger” PowerShell code as our maintenance script. One additional step needed is to create a Data Factory pipeline or two so we have something to deploy. Data Factory Testing environment resource.Azure Data Factory resource in git integrated mode (Development environment).In order to implement either a YAML or classic continuous delivery pipeline we will first need to make sure we have the tools and resources I outlined in the first post: This whole process can be easily replicated for a Production stage with an additional “Approval” step added in to delay deployment for testing and verification. Finally, the same maintenance script will run with different parameters to re-start triggers and clean up any resources not deployed from the Development ARM template.The ARM template is then deployed to the Testing Data Factory.The maintenance script will then be executed which will stop any potential triggers on the Testing Data Factory environment.This will start an agent machine inside DevOps and pull down a copy of the Data Factory code in the adf_publish branch and a copy of the maintenance file from the config repo.
![filr factory filr factory](https://4.bp.blogspot.com/-xQ_1ouV2fBg/XI5crrvuuTI/AAAAAAAABcs/8FQxADxvpS464PnDfuzFTnJ5DjomLIfUQCLcBGAs/s1600/fc1.png)
![filr factory filr factory](https://i0.wp.com/mactorrentz.com/wp-content/uploads/2022/08/spotify-cracked-pc.jpg)
It does however, bring a significantly steeper learning curve.įocussing on continuous delivery (CD) of our Data Factory in this post, I’ll walk through the steps to set up a deployment pipeline using YAML. That’s a good thing though, as your build and release pipelines are now part of your source controlled code. With the UI-based pipeline and release process being referred to as “classic” mode, it’s obvious that YAML is the way forward for your CI/CD pipelines in Azure DevOps. In Azure DevOps, YAML based build pipelines have been around for a while and with Continuous Delivery (CD) pipelines arriving in April this year (2020) all pieces of the puzzle have come together. I’ll then build upon that pipeline in subsequent posts, to test and document the Data Factory before we deploy it. I’m building the deployment stage (CD) in this post so we can get the Data Factory deployed from Development to a Testing environment. Start with my first post on CICD with Azure Data Factory for an overview on the how and why.
FILR FACTORY SERIES
This is part of a series of blog posts where I’ll build out Continuous Integration and Delivery (CI/CD) pipelines using Azure DevOps, to test, document, and deploy Azure Data Factory. Reading Time: 10 minutes Azure Data Factory – Delivery components