Enterprises often run multiple operational systems with overlapping data, which leads to fragmentation and brittle point integrations. Microsoft Dataverse and Power Platform address app, automation, and experience needs, while Fabric and OneLake provide a unified data foundation for analytics and orchestration. This session presents an integration architecture where Fabric Data Pipelines move and transform source system data into Fabric Lakehouse storage, then project curated data into Dataverse as the operational backbone for Power Apps and Power Automate.

The session contrasts Dataverse native link options with Fabric driven patterns, and explains when to favor event driven Dataverse to Fabric links versus Fabric pipelines landing external data first. Patterns for reverse flows, such as copy activities and upsert pipelines that write enriched or scored data from Fabric back into Dataverse tables, also feature in the design. Attendees see how this approach supports medallion style data layers in Fabric while still presenting a clean, governed schema in Dataverse for low code solutions.
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The session closes with design guidance on security boundaries, identity configuration, and ALM between Fabric workspaces and Power Platform environments. Example scenarios include CRM augmentation, IoT or telemetry ingestion, and advanced analytics outputs flowing into Dataverse to drive proactive workflows in Power Platform.

Learning objectives
Select when to use Fabric Data Pipelines, Dataverse link features, or both for integrating external systems into Dataverse.

Design end to end flows that land data in Fabric, standardize it through medallion layers, and surface curated entities in Dataverse for apps and automation.

Implement patterns for writing data from Fabric back into Dataverse using pipelines and upsert strategies, including identity, performance, and governance considerations.