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Microsoft Fabric Devops & CI/CD Implementation

Simform LLC

Simform enable Fabric teams with Git-based DevOps and CI/CD to automate analytics delivery, reducing risks and ensuring consistent promotion of Data Platform,Power BI, and AI assets across environment

Simform enables Fabric teams to bring discipline, automation, and control to analytics delivery by implementing a robust Git-based DevOps and CI/CD framework. This approach reduces manual deployment risks, improves traceability, and ensures consistent and reliable promotion of Fabric Data Platform, Power BI and AI assets across Dev, Test, and Production environments.

Primary Objective: Implement a baseline Git branching strategy and CI/CD deployment pipeline for Microsoft Fabric assets, enabling controlled promotion from development to production with automated checks, manual approvals, and rollback readiness.

Scope: Defining a Git branching strategy (feature, development, release, main, and hotfix), aligning Fabric workspaces to Dev/Test/Prod lifecycle stages, and configuring CI/CD pipelines using Fabric Git integration or Power BI Deployment Pipelines. It covers enabling deployment workflows for AI and advanced analytics assets alongside data and BI artifacts, implementing automated quality gates (linting, unit checks, semantic refresh, schema/data validation, reconciliation, report integrity, and AI model validation), and establishing manual approval gates such as UAT sign-off, release approvals, change records, release notes, and rollback planning. Additionally, it defines versioning and lifecycle management for AI assets, including model tracking and promotion across environments, and documents the DevOps operating model with PR reviews, merge strategy, version tagging, hotfix handling, and Main-to-Dev synchronization.

Deliverables:

  • Git branching strategy for Fabric, Power BI, and AI asset development
  • Dev/Test/Prod workspace promotion model (including AI workloads)
  • Configured CI/CD pipelines for data, BI, and AI asset deployment
  • Automated quality gate checklist (including AI validation considerations)
  • Manual approval and release governance checklist
  • Release, rollback, and hotfix process guide
  • AI asset versioning and promotion guidelines
  • Main-to-Dev synchronization approach
  • Admin and developer knowledge transfer session"

Vue d’ensemble

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