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Microsoft Fabric combines analytics workloads in one SaaS platform: OneLake supplies the shared logical data foundation, workspaces organize people and data products, and capacities provide the compute and billing resources assigned to those workspaces. The key design choice is where to place workspaces and workloads: sharing a capacity can simplify management, while separate capacities can help with workload isolation or regional requirements.
How the Fabric architecture fits together
A useful way to picture Fabric is as an organization-wide hierarchy: tenant, capacity, workspace, and items. OneLake spans the tenant as a common logical data lake rather than acting as a separate lake for each workspace. Microsoft describes Fabric’s platform and workload model in its Fabric overview and OneLake overview.
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- Tenant: The organization’s identity and administration boundary.
- Capacity: A region-bound compute and billing resource, identified by a Fabric SKU and capacity units (CUs).
- Workspace: A collaboration, access, and governance container assigned to a capacity.
- Items: The artifacts held in a workspace, such as lakehouses, warehouses, notebooks, pipelines, semantic models, and reports.
Workspaces determine how teams organize ownership, access, and lifecycle. Capacities determine the compute resource used by their assigned workspaces. One capacity can serve multiple workspaces, and a tenant can use more than one capacity. OneLake provides the common logical namespace across this structure.
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OneLake is Fabric’s tenant-wide logical data lake, built on Azure Data Lake Storage. Fabric workloads—including data integration, engineering, data science, real-time analytics, databases, warehousing, and Power BI—can use its shared data foundation. Microsoft documents Delta Parquet and Iceberg formats for tables.
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The “one copy” idea describes an intended reuse pattern: teams and workloads can work from shared data instead of maintaining a separate copy for every analytical experience. It does not guarantee that every source, operation, or workload is copy-free. For details on the OneLake model and organization, see Microsoft’s OneLake documentation.
How workspaces and capacity affect deployment
A workspace is the practical boundary for organizing related items and managing access. Its assigned capacity is the compute resource serving its work. The deployment pattern therefore affects governance, regional placement, workload contention, and cost management—not just folder structure. Microsoft’s Fabric deployment-pattern guidance outlines these architectural trade-offs.
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Share a capacity across workspaces
Putting multiple workspaces on one capacity can consolidate management and capacity use. The trade-off is that workloads share the same compute allocation: contention can affect query and job performance. A busy engineering workload, for example, may compete with interactive analytics on that capacity.
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Separate workloads across capacities
Separate capacities can provide more workload isolation and can support regional placement requirements. They also create additional resources to manage and allocate. Capacities are region-bound, so placement should be planned with data location and residency requirements in mind; do not assume a workspace can use any capacity regardless of region.
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Questions to settle before assigning workspaces
- Which team owns each workspace, and who needs access to its items?
- Do workloads need isolation from one another, or is shared capacity acceptable?
- Are there regional placement or data-residency requirements?
- How will capacity usage be monitored and managed, including scale or pause decisions where available?
- How many workspaces are needed for team boundaries, development stages, and lifecycle management?
Choosing a lakehouse, warehouse, or Direct Lake
These terms describe different parts or patterns of a Fabric solution, not interchangeable names for the same thing. Microsoft’s storage options guidance and lakehouse and warehouse decision guide frame the choice around workload needs.
| Option | What it is | Useful fit | Important consideration |
|---|---|---|---|
| Lakehouse | Combines file storage and tables in OneLake; Fabric provisions a SQL analytics endpoint for querying its Delta tables. | Lake-oriented engineering and work that benefits from combining files and tables. | Choose it based on data shape, engineering patterns, SQL needs, governance, and operational preferences. |
| Warehouse | A relational analytics storage experience. | Relational analytics patterns where the warehouse experience suits the team’s SQL and operational approach. | It is not a universal default over a lakehouse; compare workload and governance requirements. |
| Direct Lake | A Power BI semantic model storage mode for Delta tables in OneLake. | Interactive analysis designed to avoid a full Import-mode copy and refresh workflow. | Requires Fabric capacity, has SKU-specific limits and guardrails, and table tuning matters. |
Direct Lake is a semantic-model approach, not a replacement label for the underlying lakehouse or warehouse. It does not mean there is no capacity cost or no need to tune tables. Microsoft documents the mode and its constraints in the Direct Lake overview.
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How OneLake storage charges differ from capacity consumption
Storage and compute-related consumption are distinct. Microsoft’s OneLake consumption documentation says storage is billed pay-as-you-go per GB and does not consume Fabric CUs, while OneLake transactions consume capacity units. Capacity usage and storage attribution can depend on where data resides and which capacity performs access. Excess consumption can lead to throttling. See Microsoft’s OneLake consumption guidance and capacity consumption example.
Because storage billing and capacity consumption are separate, storage volume alone does not describe the full cost picture. Exact rates depend on the relevant region and SKU; check Microsoft’s current pricing for the deployment rather than applying a generic price.
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A practical way to plan the architecture
- Set organizational boundaries: Identify the tenant-level administration and the teams or domains that need distinct workspace ownership.
- Choose workspace boundaries: Group items that share owners, access rules, and lifecycle needs; avoid treating each workspace as its own OneLake.
- Decide capacity placement: Assign workspaces to capacities according to region, workload isolation, expected contention, and management needs.
- Select the storage experience: Use lakehouse or warehouse patterns based on data and operational requirements rather than assuming one is always better.
- Plan Power BI access: If Direct Lake is appropriate for Delta tables in OneLake, validate capacity eligibility, SKU-specific limits, and table tuning needs.
- Track both sides of consumption: Account for OneLake storage billing separately from transaction and capacity-unit use.
For guided practice with discovery and connection workflows, Microsoft Learning provides the Discover and connect to data in OneLake lab.
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