Getting Started — Microsoft Fabric Guide

👤 Who is this for?

Everyone — Track your Fabric adoption progress across foundation setup, security, architecture, deployment, and analytics. Progress is saved in your browser.

Getting Started

Getting Started Checklist

Track your Fabric adoption progress. Your progress is saved in your browser.

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Foundation Establish the platform guardrails

Outcome: Fabric has accountable owners, controlled access, a workspace structure, and the minimum tenant controls required to begin safely.

Activate a Fabric trial or provision a capacity Start with a free 60-day trial (F64) to evaluate features
Configure tenant-level Fabric settings Enable/disable Fabric for specific security groups in the admin portal
Create workspace structure (Dev / Test / Prod) Follow naming conventions and assign to appropriate capacity
Set up domains and assign workspace ownership Align workspaces to business domains (Finance, Marketing, etc.)
Configure workspace roles and access Assign Admin, Member, Contributor, and Viewer roles per workspace
Enable audit logging Turn on unified audit log in Microsoft 365 compliance center

Foundation exit criteria

A named platform owner can identify the tenant configuration, capacity, workspace ownership, domain mapping, access groups, and audit location.

Adopt Deliver the first governed workload

Outcome: a representative use case moves from source data to a trusted analytical product using an intentional architecture.

Design your medallion architecture (Bronze / Silver / Gold) Define layers, naming conventions, and data flow patterns
Create lakehouses for each medallion layer e.g., lh_bronze, lh_silver, lh_gold
Decide: Lakehouse vs. Warehouse for Gold layer Choose based on team skills (Spark vs. T-SQL) and consumer needs
Set up shortcuts for external data sources Connect to ADLS Gen2, S3, or cross-workspace data without copying
Build your first data ingestion pipeline Use Data Factory to load data into Bronze layer lakehouse
Create transformation notebooks (Bronze → Silver → Gold) Implement data cleansing, deduplication, and business logic

Adopt exit criteria

The pilot produces a validated business output, has an owner and support path, and can be rerun without undocumented manual intervention.

Mature Make delivery governed and repeatable

Outcome: teams use consistent cataloging, protection, version control, deployment, and trust practices across production workloads.

Enable Microsoft Purview integration Set up data cataloging, lineage tracking, and glossary
Apply sensitivity labels to sensitive data Configure MIP labels and auto-labeling policies
Connect workspaces to Git (Azure DevOps / GitHub) Enable version control and collaboration on Fabric artifacts
Set up deployment pipelines (Dev → Test → Prod) Configure stages and deployment rules for environment-specific settings
Implement Row-Level Security on reports Configure RLS roles for multi-tenant or role-based data access
Endorse production artifacts (Certify / Promote) Signal trust and quality for your Gold-layer datasets and reports

Mature exit criteria

Production changes are traceable, trusted assets are discoverable, sensitive data is classified, and deployment does not depend on direct edits in production.

Optimize Improve efficiency and expand value

Outcome: teams use telemetry and trusted semantic assets to improve performance, cost, reliability, and AI readiness.

Create a Direct Lake semantic model on Gold layer Connect Power BI to lakehouse/warehouse for optimal performance
Install the Capacity Metrics app Monitor CU consumption, identify throttling, and optimize costs
Prepare semantic models for AI (Copilot & Data Agents) Set AI data schema, add AI instructions, create verified answers — see guide

Optimize exit criteria

Capacity and workload decisions are telemetry-driven, performance targets are monitored, and new capabilities are introduced through measurable experiments rather than platform-wide assumptions.

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