azure-ci-cd.txt

Setting up a CI/CD pipeline using Azure Actions in Azure involves several steps. Here is an overview of the process:

Create a new repository or navigate to an existing one in Azure DevOps.

Create a new pipeline by clicking on the "Pipelines" tab in the left-hand menu and then clicking "New pipeline."

Choose the type of code repository you are using (e.g., GitHub, Azure Repos), and then select the repository you want to use.

Choose the type of pipeline you want to create (e.g., YAML, Classic editor).

Configure your pipeline by defining your build and release stages, including any necessary tasks such as building, testing, and deploying your application.

Set up any necessary variables or parameters to ensure your pipeline runs correctly.

Configure your triggers to automatically run your pipeline when changes are made to your code.

Save and run your pipeline to ensure that it is working correctly.


Do same for Python and Azure Data Factory. How to setup ci cd pipeline?


Here's an overview of the process:

Create a new pipeline in Azure Data Factory.

Add a new activity to the pipeline and choose "Custom activity."

In the "Custom activity" settings, specify the command to run your Python script. For example:

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python my_etl_script.py
Create a new Azure Container Registry to store your Docker images.

Build a Docker image of your Python ETL service using a Dockerfile. Here's an example Dockerfile:

bash
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FROM python:3.8

COPY . /app
WORKDIR /app

RUN pip install -r requirements.txt

CMD ["python", "my_etl_script.py"]
Push the Docker image to the Azure Container Registry.

Create a new YAML pipeline in Azure DevOps.

In the pipeline, specify the Docker image to use and any necessary environment variables.

Add a step to deploy the Docker image to an Azure Kubernetes Service (AKS) cluster. Here's an example of what this might look like:

yaml
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- stage: deploy
  displayName: 'Deploy to AKS'
  jobs:
  - job: deploy
    pool:
      vmImage: 'ubuntu-latest'
    steps:
    - task: KubernetesManifest@0
      displayName: 'Deploy to AKS'
      inputs:
        action: deploy
        namespace: my-namespace
        manifests: |
          apiVersion: apps/v1
          kind: Deployment
          metadata:
            name: my-etl-service
          spec:
            replicas: 1
            selector:
              matchLabels:
                app: my-etl-service
            template:
              metadata:
                labels:
                  app: my-etl-service
              spec:
                containers:
                - name: my-etl-service
                  image: myregistry.azurecr.io/my-etl-service:latest
                  env:
                  - name: MY_VAR
                    value: 'my_value'
Set up a trigger to automatically run the pipeline when changes are made to your code.
That's it! This should give you a basic idea of how to set up a CI/CD pipeline for Python ETL services using Azure Data Factory. Of course, the specific details will depend on your particular requirements and environment, so you may need to customize this approach accordingly.