Data Factory CI/CD in Azure DevOps

Azure Pipeline is consist of two parts: pipeline and release. They represent
CI and CD separately.

  1. Build Pipeline – to build and test the code. The build creates an artifact that’s used by the rest of your pipeline to run tasks such as deploying to staging or production. 
  2. Release Pipeline – once the code is updated, built and packaged, it can be deployed to target services using Release Pipelines. 
top half: CI pipeline; down half: CD pipeline

Let’s talk about how to implement this process for Azure data factory. Before you start, I suppose your current ADF has matched these requirements (if not, please refer to here):

  • You already have a Azure Repos.
  • ADF is integrated with this Git Repos.
  • A key Vault. which is used for storing database and datalake connection information, plus all configuration parameters for release.

The whole ADF CI/CD pipeline is like this:

Building Pipeline (CI)

  • Create new pipeline, choose “adf_publish” for default branch
    • “adf_publish” branch is created by ADF automatically, after you click “publish” in your ADF GUI.
the three json files are used to set parameters in the different environments
  • Add new Agent job
  • search for “Data factory”, add a new publish artifacts
    • set the path to publish(git path)
    • set artifact publish location as Azure pipelines

Release Pipeline (CD)

left part: CI right part: CD

So next step is deploying artifact into three environments: DEV, QA and PROD.

use key vault to set sQL Server in pipeline
  • Import Azure Key Vault. where you stored all the connection information. We would use these information in two areas.
    • Release pipeline: get the basic information in each environment, e.g, database name, data lake name, etc. see the screenshot above.
    • template parameters used in adf_publish.
  • Set the Datafactory.
    • Add a “Azure Resource group deployment”. where we need to focus one the template path.
we have to set the ARM template and parameters which point to adf_publish branch
  • How to set up adf_publish
    • go back to azure repos.
    • switch to adf_publish.
    • create corresponding ARM parameter template for each environment. You can find an example blew.
the three json files are used to set parameters in the different environments
{
	"$schema": "https://schema.management.azure.com/schemas/2015-01-01/deploymentParameters.json#",
	"contentVersion": "1.0.0.0",
	"parameters": {
		"factoryName": {
			"value": "datafactory name"
		},
		"LS_AKV_KeyVault_properties_typeProperties_baseUrl": {
			"value": "https://XXXXkeyvault.vault.azure.net/"
		},
		"LS_SQL_ConfigDb_properties_typeProperties_connectionString_secretName": {
			"value": "sqlconnection"
		},
		"LS_DLS_Datalake_properties_typeProperties_url": {
			"value": "https://XXXXXdatalake.dfs.core.windows.net/"
		}
	}
}

If we need enable Continuous deployment trigger , we have to link with adf_publish branch as well.

click the little logo in release pipeline page.
set to adf_publish

Reference:

Azure DevOps Pipelines. https://docs.microsoft.com/en-us/azure/devops/pipelines/?view=azure-devops

Continuous integration and delivery in Azure Data Factory. https://docs.microsoft.com/en-us/azure/data-factory/continuous-integration-deployment

Use Azure Key Vault to pass secure parameter value during deployment. https://docs.microsoft.com/en-us/azure/azure-resource-manager/templates/key-vault-parameter?tabs=azure-cli

Simple way to create Gantt chart for Azure Devops through power BI

usually we use Gantt chart to track the project progress, take the picture blew for example, the dark part means completed, the light part means waiting for being done.

To reach this result, there are two solutions. The steps in azure devops are same for both solutions.

  • Add start date and end date for each tasks. The Admin can change the layout to make these two fields visible on edit page, but I can only change them in backlogs
  • Add Efforts information: estimate time, already done and left hours. The picture blow shows I have done 80% ‘s workload.
  • Create a view for listing all user stories and tasks. ( one time effort for extracting the data)

Power BI steps:

after imported, you will find gantt chart
  • Import data through “Get Data- More – Online Service – Azure Devops(beta)”, then choose the view you created in Devops.
  • set the Task, start date, end date, % complete for Gantt chat.