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How the connection model works
OneLake is Fabric’s shared data lake and access layer. Fabric experiences and analytics engines can work with data through a common namespace, while the data itself may be referenced in place, mirrored or replicated according to its source, or ingested into Fabric. Those approaches can coexist in one architecture.
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A shortcut is a reference to selected data in another location, such as a file, folder, or table. It is not automatically a second copy of the referenced data. Mirroring, by contrast, adds an external database or catalog to Fabric; the precise behavior depends on the source and may involve in-place access or replication. Data movement remains available when a workload needs a managed copy, transformation, or a connection method the other options do not provide.
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| Method | What it does | Good fit when | Check before implementation |
|---|---|---|---|
| OneLake shortcut | References selected supported data in an internal or external location. | You want Fabric workloads to access selected data through OneLake without automatically creating an initial copy. | Source and format support, credentials, permissions, caching, identity mode, workload support, and the effect of moving or deleting the target. |
| Mirroring | Adds an external database or catalog to Fabric; behavior varies by source and may access data in place or replicate it. | You need database- or catalog-level integration and the source is supported. | Whether the source is accessed in place or replicated, which objects are supported, expected latency, and resulting storage and operational behavior. |
| Data Factory connectors, pipelines, Copy activities, or Copy job | Connects to sources and moves data into Fabric; pipelines can also support transformation. | You require managed movement or transformation, or the source is not a suitable shortcut or mirroring target. | Connector support, refresh and latency requirements, transformation needs, data residency, and ongoing pipeline operations. |
| Eventstreams and other ingestion paths | Provide additional ways to bring event or other source data into Fabric. | The source and workload call for an ingestion path suited to event data or another specific pattern. | Source compatibility, latency and processing requirements, and the operational behavior of the selected path. |
| Power Apps Link to Microsoft Fabric | Makes Dynamics 365 and Power Apps data available in OneLake through Dataverse shortcuts. | You are connecting Dataverse data to Fabric analytics and want to use this direct-link pattern. | The Dataverse shortcut is read-only; it is not a write-back path for an application. |
Microsoft’s documentation describes shortcut sources including Azure storage, Amazon S3, Iceberg-compatible sources, Dataverse, and on-premises locations. That list is not a guarantee that every source, format, authentication mode, or workload is supported in every deployment; check the current prerequisites for the specific connection.
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Design the flow from source to consumer
- Inventory the source and the outcome. Identify the source type, which data is needed, the consuming Fabric workload, freshness expectations, transformation requirements, and any residency or operational constraints.
- Select a connection pattern. Use a shortcut when referencing supported data meets the need; consider mirroring for supported database or catalog integration; use pipelines, Copy, Data Factory connectors, Eventstreams, or another ingestion path when managed movement or a different source pattern is required.
- Plan access and identity before exposing data. Define who or what can access the source and the resulting data. A shortcut does not necessarily use each end user’s identity: behavior depends on its identity mode, credentials, source, and consuming workload.
- Organize and curate data for reuse. One documented Fabric architecture uses bronze, silver, and gold layers: retain raw source data, conform and reuse datasets, then publish curated models for analytics. Treat this as an example, not a mandatory design for every organization.
- Govern consumption and change. Apply permissions, lineage, deployment controls, and oversight to the data flow and its outputs. Governed models can support Power BI, data agents, Copilot, and operational reporting in Microsoft’s reference architecture.
When Dataverse is the source
Power Apps Link to Microsoft Fabric makes Dynamics 365 and Power Apps data available in OneLake through shortcuts while the source data remains in Dataverse. Microsoft describes this direct-link pattern as avoiding the need to build an export and ETL process for that integration. The resulting Dataverse shortcuts are read-only, so an app that must write changes back to Dataverse needs a separate write path.
Do not confuse that source-to-Fabric pattern with exposing Fabric data to Power Platform apps. A separate documented approach uses Dataverse virtual tables to make Fabric lakehouse data available to apps and flows. They address opposite directions of access and are not interchangeable.
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Build identity and governance into the design
Access control is not a final step after connecting sources. Decide how source credentials are supplied, which users and workloads are authorized, and how permissions apply across the connection and consumption layers. Shortcuts have identity-mode limitations, and their credential behavior can differ by source and workload; validate the selected configuration rather than assuming access always passes through the end user’s identity.
- Permissions: Define access at the relevant source and Fabric layers, including role-based access control where appropriate.
- Lineage: Track how source data feeds curated datasets and published models.
- Deployment: Include deployment controls in the lifecycle for data assets and models.
- Consumption: Publish governed semantic models and outputs for the intended analytics and operational uses.
Microsoft’s reference architecture combines identity, RBAC, lineage, deployment practices, and certified semantic models across ingestion, data layers, and consumption. It is illustrative rather than a universal prescribed blueprint; adapt the controls and layers to the organization’s sources and workloads.
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Common design mistakes to avoid
- Treating a shortcut as a guaranteed end-user pass-through. Verify the identity mode and credential behavior for the particular source and workload.
- Assuming mirroring always means the same thing. Confirm whether the specific source is accessed in place or replicated and what objects and operations are supported.
- Choosing no-copy access when movement is required. If transformation, managed refresh, residency, or source compatibility calls for moving data, use an appropriate ingestion path instead.
- Using the Dataverse link as application write-back. Dataverse shortcuts are read-only; treat analytics access and application updates as separate requirements.
- Copying a reference architecture without validating fit. Bronze, silver, and gold layers and particular governance controls are documented patterns, not requirements for every deployment.
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