I am currently using Jenkins for my pipelines and I am also using the shared libraries written in groovy. Now I am planning to move to Azure Devops and use the Azure Pipelines. Is there any way we can use the same groovy pipelines and the shared libraries in Azure Pipelines or do I need to convert all of them to yml files from groovy. And is there any automated way to convert to yml or I need to convert all the groovy to yml manually?
If you want to move the CI/CD pipelines for your project to Azure DevOps, you need to set up the pipelines on Azure DevOps following the syntax supported in Azure Pipelines. If you want to set up the YAML pipelines, you can reference the documents about "YAML schema".
For the libraries required in your project:
If the libraries are built and maintained by yourself, you can build and publish the libraries to a Maven feed on Azure Artifacts. For more details, you can reference the documents about "Maven packages".
If the libraries are shared by others, you can publish the library files to a universal feed. For more details, you can reference the documents about "Universal packages".
After above, in the CI/CD pipelines on Azure DevOps, you can restore the packages from the Artifact feed to your project.
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I have an Azure Repos project with IaC code and ci/cd yaml pipelines to set up Azure Synapse infrastructure. Can you recommend what is right approach when I integrate the workspace to connect to git? Should I create a new project in Azure Repos for the Synapse artifacts or should I use the same repository as the infrastructure project?
I will be setting up ci/cd pipelines to deploy the azure synapse artifacts as well.
Thanks!
Here's an answer to a similar question I posted:
You'll want to follow Microsoft's guide on CI/CD with Synapse. This is a great walkthrough video for the process.
Work in your development environment and commit to your collaboration branch.
A pipeline triggers off of your workspace_publish branch, releasing your code to your next environment (preferably with an approval gate)
Continue releasing the same code to higher environments, again with approval gates.
For Azure DevOps, you can use variable groups to parameterize your pipeline. Also make sure to read through the custom parameters section on that link to parameterize parts of the template that are not parameterized by default.
im trying to deploy some synapse artifacts to a synapse workspace with devops repo integration via a python runbook. By using the azure-synapse-artifacts library of the python azure sdk the artifacts are published directly to the live mode of the synapse workspace. Is there any way to deploy artifacts to a devops repo branch for synapse? Didnt find any devops repo apis or libaries, just for the direct integration of git.
We can use CICD in this case, as this process will help to move entities from one environment to others, and for this we need to configure our synapse work space as source in GIT.
Below are few straight steps we can follow:
Set up Azure Synapse workspace and configure pipeline in Azure Devops.
Under staging while creating DevOps project, we can select Add Artifacts and select GIT.
Configure the workflow file and add workflow.
You can refer to MS Docs for detailed explanation of each step in achieving this task
I'm trying to figure out how viable Azure ML in production; I would like to accomplish the following:
Specify custom environments for my pipelines using a pip file and use them in a pipeline
Declaratively specify my workspace, environments and pipelines in an Azure DevOps repo
Reproducibly deploy my Azure ML workspace to my subscription using an Azure DevOps pipeline
I found an explanation of how to specify environments using notebooks but this seems ill-suited for the second and third requirements I have.
Currently, we have a python script, pipeline.py that uses the azureml-sdkto create, register and run all of our ML artifacts (envs, pipelines, models). We call this script in our Azure DevOps CI pipeline with a Python Script task after building the right pip env from the requirements file in our repo.
However, it is worth noting there is YAML support for ML artifact definition. Though I don't know if the existing support will cover all of your bases (though that is the plan).
Here's some great docs from MSFT to get you started:
GitHub Template repo of an end-to-end example of ML pipeline + deployment
How to define/create an environment (using Pip or Conda) and use it in a remote compute context
Azure Pipelines guidance on CI/CD for ML Service
Defining ML pipelines in YAML
Azure DevOps Pipeline : How to release Project binary file into Azure.
How can I deploy project binary files into server using Azure Pipeline, don't want to release project full source code?
You would do something like this.
create a deployment group
create a build, configure it
create a release, configure it (get artifacts from build, deploy to deployment group)
These are the steps you need to perform. link has more information on this matter
I want to do continuous integration and deployment in Azure Data factory. I'm not able to find any specific document explaining this.
How can I do it or where can I read about it?
To build your project, you can use msbuild - just like it's done in Visual Studio. It will validate syntax, check references between json configurations and check all dependencies. If you are using Visual Studio Team Services as CI server you can use Visual Studio Build step in build configuration to do it. However, it requires to install ADF tools for VS on build agent machine.
To deploy, you can try:
Powershell. For example, you can use Set-AzureRmDataFactoryV2Dataset to deploy datasets. There are similar commands for all other configurations and for version 1 of Azure Data Factory as well.
If you are using VSTS, you can try this 3rd party extension. It allows to deploy json configurations and start/pause pipelines. I'm not sure if it works with ADF v2.
You can use the VSTS GIT integration with ADF v2 UX to do continous deployment and continuous integration. VSTS GIT integration allows you to choose a feature/development branch or create a new one in your VSTS GIT repo. You can work in your feature/development branch and create PR in VSTS GIT to merge your changes to the master branch. You can then publish to your data factory using ADF v2 UX. Please try this and let us know if this doesn't work for you or you face any issues.