Deployment Pipelines

Plan and Implement a Data Analytics Environment with Microsoft Fabric

Shahzad Mian

Content Developer, DataCamp

Pipelines like a conveyor belt

Conveyor Belt

Plan and Implement a Data Analytics Environment with Microsoft Fabric

Why Pipelines Matter

  1. Structured changes order
  2. Clear audit log
  3. Easy to analyze changes
  4. Automated process
  5. Consistency and reliability

Factory Pipeline

Plan and Implement a Data Analytics Environment with Microsoft Fabric

Example of a Deploy

Without Deployment Pipelines

  1. Save changes in the original workspace
  2. Compare version between the two workspaces
  3. Publish new changes in the new workspace
  4. Check for errors or missing items

With Deployment Pipelines

Example of Deploy Button

1 Credits: https://learn.microsoft.com/en-us/fabric/cicd/deployment-pipelines/intro-to-deployment-pipelines
Plan and Implement a Data Analytics Environment with Microsoft Fabric

Understanding Pairing

Paired item comparison in Fabric

Paired Items are overwritten (status "Same as source")

Unpaired Items will create a new copy in the target stage (status "Only in source")

1 Credits: https://learn.microsoft.com/en-us/fabric/cicd/deployment-pipelines/intro-to-deployment-pipelines
Plan and Implement a Data Analytics Environment with Microsoft Fabric

Let's practice!

Plan and Implement a Data Analytics Environment with Microsoft Fabric

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