Azure Data Factory Deployemnt || GIT Repository || Azure Devops скачать в хорошем качестве

Azure Data Factory Deployemnt || GIT Repository || Azure Devops

To set up a CI/CD pipeline for Azure Data Factory (ADF) using Azure DevOps and a Git repository, you can follow these general steps: Set up a Git repository: Create a Git repository in Azure DevOps where you will store your ADF pipeline code. You can create a new repository or use an existing one. Create an Azure Data Factory: Set up an Azure Data Factory instance in the Azure portal if you haven't done so already. Note down the details like the name and resource group of your ADF instance. Create an Azure DevOps project: Create a new Azure DevOps project if you don't have one already. This project will contain your CI/CD pipeline. Create a new pipeline: In your Azure DevOps project, go to Pipelines and click on "New Pipeline." Select the source control system where your Git repository resides (e.g., Azure Repos Git). Configure the pipeline: Choose the repository and branch containing your ADF pipeline code. Azure DevOps will analyze the repository and suggest templates. Select the "Azure Data Factory" template that matches your needs. Connect to Azure Data Factory: In the pipeline YAML file, you'll see a section with Azure Data Factory settings. Configure the subscription, resource group, and factory name to connect your pipeline to the correct ADF instance. Define build and release stages: Customize your CI/CD pipeline according to your requirements. You can specify stages for building, testing, and deploying your ADF pipeline. Each stage can consist of one or more tasks. Configure deployment triggers: Set up triggers to control when the pipeline should run. For example, you might trigger the pipeline on each commit to a specific branch or on a schedule. Define pipeline variables: Declare any necessary variables in your pipeline, such as connection strings or environment-specific settings. You can define these variables in the Azure DevOps pipeline UI or directly in the pipeline YAML file. Save and run the pipeline: Save your pipeline configuration, and trigger an initial run to verify everything is set up correctly. Monitor the pipeline's progress and make any necessary adjustments. Monitor and manage: Once the pipeline is running, you can monitor its status and view the logs to troubleshoot any issues. You can also manage the pipeline's configurations, triggers, and variables as needed. By following these steps, you can create a CI/CD pipeline for deploying Azure Data Factory pipelines using Azure DevOps and a Git repository. Remember to adapt the pipeline stages and tasks to your specific requirements, such as building, testing, and deploying your ADF pipeline artifacts.