Deployment Stages in Fabric

Plan and Implement a Data Analytics Environment with Microsoft Fabric

Shahzad Mian

Content Developer, DataCamp

Introduction to Deployment Stages

Example of direct deployment in production

Example of good deployment stage in production

Plan and Implement a Data Analytics Environment with Microsoft Fabric

Deployment Stages in Fabric

Fabric Deployment Stages screen

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

Development Stage

  • Experimentation Freedom
  • Rapid Iteration
  • No Production Impact

Tools

Plan and Implement a Data Analytics Environment with Microsoft Fabric

Testing Stage

Laboratory test

  • Validation Phase with Production-like data
  • Bug Detection and Performance Assessment
  • User Acceptance Testing (UAT)
Plan and Implement a Data Analytics Environment with Microsoft Fabric

Production Stage

  • All Users Access
  • Stable Release
  • Strict Security Controls

Stage Theater

Plan and Implement a Data Analytics Environment with Microsoft Fabric

Why Deployment Stages are important?

  • Prevents bugs from affecting all users at once
  • Reduces business risk by testing before full release
  • Makes it easier to catch and fix issues early
  • Allows teams to work safely without breaking live systems
  • Provides controlled environment for testing new features
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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