Microsoft Fabric vs Databricks for Payer Data Teams

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The Fabric versus Databricks debate gets framed as a tech preference. It isn't. It's an architectural allocation question with direct consequences for risk adjustment revenue, STARS performance, and CMS submission timelines. In this article, we'll go over where each platform wins, where the boundaries should sit, and how to build the internal case for a workload-based allocation decision.

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Why This Is an Architectural Allocation Question

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The payer data stack has two distinct zones. 

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The first is transformation: 

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• Parsing EDI files
• Normalizing claims
• Resolving member identity
• Running risk adjustment pipelines

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The second is delivery: 

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• Surfacing RAF trend reports 
• STARS dashboards
• MLR views
• Executive scorecards

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Both platforms can technically operate in both zones which is exactly what makes this decision hard. Microsoft itself describes Databricks and Fabric as a unified and scalable analytics ecosystem, acknowledging that mature teams often run both. The question is which workloads belong where.

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The Three Conditions That Trigger This Evaluation at Health Plans

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Three events typically force this decision: 

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1. The first is an enterprise data warehouse (EDW) modernization initiative where legacy infrastructure can no longer handle 834/835/837 feeds, CMS files like MAO-004 and MMR, and HIE ADT streams at scale. 
2. The second is a risk adjustment or STARS performance gap where timely HCC submissions and quality metric delivery are breaking down. 
3. The third is a merger, acquisition, or PE recapitalization where a defensible architecture narrative is required. 

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Each trigger implies different platform weighting like:

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• EDW modernization favors transformation depth
• BI delivery gaps favor Fabric's convergence story
• Exit-driven events favor total cost of ownership

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How Existing Azure and Microsoft Licensing Agreements Distort the Decision

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Fabric Capacity Units draw against existing Microsoft Azure Consumption Commitments, and legacy Power BI Premium SKUs have been grandfathered into Fabric capacity. So conversion looks cost-neutral on paper. It is not. Fabric's fixed-capacity pricing model can run 4 to 9 times more expensive than comparable Databricks pay-as-you-go usage for bursty or variable workloads. Plans that commit based on licensing optics often discover their transformation-heavy pipelines run poorly on Fabric's current compute architecture, and that discount becomes a migration liability.

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Where Databricks Wins in Payer Data Environments

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837 and 835 EDI Processing at Scale with Spark-Based Pipelines

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The 837 carries procedure codes. The 835 carries diagnosis codes, remittance data, and the claim-level detail that feeds medical expense reporting and RAF scoring. These files are large, semi-structured, and require custom parsing at volume. 

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Databricks' autoscaling Spark clusters handle EDI ingestion natively. Fabric's capacity is a fixed pool of cores. Workload spikes during year-end encounter processing or CMS submission windows cause queries to queue. Databricks lets organizations spin up a large transient cluster for a bulk load and tear it down after. At hundreds of millions of claim lines across dozens of payer feeds, that elasticity matters.

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HCC Suspect Identification and Risk Adjustment ML Orchestration

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HCC suspect identification is an ML-heavy problem. It pulls from historical diagnoses, pharmacy signals, and chart review data to flag undocumented conditions before a coding window closes. Microsoft Fabric handles this workload through its Data Science experience, which includes native MLflow integration, Spark-based compute, and Delta Lake storage via OneLake. 

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Feature engineering, model training, and experiment tracking all run within a single governed environment. Plans generating HCC suspect lists and scoring RAF impact by cohort can operationalize that pipeline without leaving the Fabric workspace.

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Incremental vs Full Refresh Logic for Claims and Eligibility Tables

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Eligibility tables often need full refreshes because attribution and coverage status change retroactively. Claims tables support incremental logic, but require handling of 30 to 60 day claims lag windows. In well-designed medallion architectures, switching from a full reload to an incremental Delta merge on a 50-million-row claims table can drop compute time from hours to minutes. Databricks' Delta Lake provides precise control over this through time travel and MERGE operations. Fabric supports Delta too, but its auto-throttling on shared capacity means large sprint loads can disrupt BI users sharing the same pool.

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MLflow and ML Maturity for Active Population Health and Gap Closure Models

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Effective gap closure programs need population health models that score member responsiveness and prioritize gaps by STARS weight. MLflow in Databricks delivers reproducible experiment tracking, model registry, and deployment pipelines connected directly to the feature store. Fabric's ML support is still developing. For any team running active ML-driven interventions against its member population, Databricks is the more mature environment.

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Where Microsoft Fabric Wins in Payer Data Environments

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Shortening the Path from Clean Data to Operational BI Delivery

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Not every payer data problem is a transformation problem. When a VP of Quality needs a STARS gap closure rate by provider group by end of week, speed of delivery is the problem. Fabric's Direct Lake mode lets Power BI hit Delta Lake tables directly combining in-memory aggregation performance with near-real-time freshness, without a separate ETL step to populate a semantic layer. 

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Power BI on Databricks typically requires DirectQuery against a SQL Warehouse, adding latency that compounds on large payer datasets. When the data is already clean, Fabric removes the handoff friction that slows BI delivery.

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OneLake Architecture and the Power BI Convergence Argument

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All Fabric workloads, lakehouses, warehouses, and tabular models share a single logical storage container called OneLake. A Spark job writes a Delta table once, and both Fabric SQL warehouses and Power BI Direct Lake queries read from the same source without copying. 

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A gold-layer eligibility table lands in OneLake and BI users see it in Power BI immediately. For teams managing eligibility, claims, CMS files, and quality measures across multiple domains, that eliminates the sync overhead that accumulates in multi-tool stacks.

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The Case for Fabric When Fragmented BI Delivery Is the Primary Pain

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Some plans have functional ETL but a fragmented BI layer: five versions of the STARS dashboard, three MLR definitions, no shared semantic layer. Fabric addresses this directly. Power BI is a core experience in Fabric, not a bolt-on. Consolidating siloed Excel workbooks, legacy SSRS reports, and disconnected Power BI workspaces into a single governed environment is a realistic project, not a multi-year effort.

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MLR Dashboards, RAF Trend Reporting, and STARS Performance Views in Fabric

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Medical Loss Ratio dashboards, RAF trend reports by plan year, and STARS performance scorecards do not need Spark. They need trustworthy data in a governed semantic model surfaced quickly to the people who make decisions. Fabric's Power BI-native delivery, row-level security, and certified dataset model are purpose-built for this layer.

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The Eligibility and EMPI Layer: Which Platform Owns Your Source of Truth

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Why Eligibility Architecture Determines EDW Platform Strategy

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Eligibility is the source of truth from which everything downstream derives meaning. Claims without eligibility context cannot be attributed. Risk adjustment submissions without valid eligibility anchoring are rejected. STARS measures without accurate PCP attribution report against the wrong provider. An eligibility error is not a data quality problem in isolation. Instead it is a revenue integrity problem that cascades across every CMS-facing pipeline.

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Slowly Changing Dimensions and Member Identity Resolution Across Systems

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Member coverage changes retroactively. Managing this as SCD Type 2 preserving historical state across corrections  requires reliable MERGE operations and tight incremental pipeline control. Databricks' Delta Lake handles SCD Type 2 with established production patterns that payer engineering teams have been running for years. Fabric supports similar logic but is less battle-tested in payer contexts, where retroactive eligibility corrections and late enrollment files are routine.

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Cross-Domain Joins That Make STARS and Risk Adjustment Reporting Reliable

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STARS reporting joins eligibility, claims, HCC submissions, and quality measures across time. RAF calculations join risk scores against eligibility snapshots at specific plan year dates. OneLake simplifies this because all domains share the same underlying Delta storage. A single SQL join covers the join without additional data movement. For massive tables with frequent incremental updates, Databricks handles churn better. For monthly snapshots queried repeatedly for reporting, Fabric's SQL layer is efficient.

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EMPI Integration Considerations for Fabric vs Databricks

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A member identity fragmentation problem has the same member appearing across an EMR, a payer eligibility file, and an HIE feed under different names and IDs. An Enterprise Master Patient Index (EMPI) solves this. Running EMPI resolution requires probabilistic matching at scale. Databricks handles this well through its ML runtime. Fabric can ingest EMPI output but is a less natural fit for running the resolution logic itself. If EMPI services are already on Azure, the integration tilts toward Fabric. If a plan uses a third-party EMPI with heavy batch analytics requirements, Databricks fits more cleanly.

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Power BI on Databricks vs Power BI Native in Fabric

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DirectQuery and Import Mode Tradeoffs for Payer Analytics Teams

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Power BI on Databricks uses DirectQuery against a SQL Warehouse live data, but query latency that compounds on eligibility tables with tens of millions of rows. Import mode solves latency but creates stale data risk between refresh windows. Fabric's Direct Lake mode addresses both: in-memory aggregations without copying data out of OneLake. For daily STARS dashboards or RAF trend views querying billions of rows, that proximity is a real operational advantage.

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Semantic Layer Consolidation in OneLake: Real Value Proposition vs Migration Cost

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Consolidating semantic models into OneLake is genuinely valuable, but migration cost is real. Migrating Power BI datasets from Databricks requires revalidating measure logic, rebuilding row-level security, and re-certifying datasets. For teams with five or fewer core reporting domains  eligibility, claims, risk adjustment, quality, financials the consolidation pays off. For teams with dozens of semi-independent workstreams, running Fabric side-by-side via OneLake shortcuts into Databricks data is often the right interim posture.

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Governance and Lineage Tradeoffs When Collapsing the BI Layer Into Fabric

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Collapsing BI into Fabric centralizes governance under OneLake and Purview. Changes in underlying data reflect immediately in report metadata and access is managed in one place. But Databricks' Unity Catalog provides column-level lineage across the full engineering stack tracing a RAF score back through every transformation to the source EDI file. For organizations under CMS audit pressure, that lineage depth is not optional, and it is an area where Unity Catalog currently leads Fabric.

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Total Cost of Ownership Beyond License Cost

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Migration Effort and Pipeline Rebuild Risk in a Payer Production Environment

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Platform migrations in payer environments carry compliance risks that generic TCO frameworks miss. Claims pipelines cannot go dark. Eligibility refreshes cannot miss monthly cadences without triggering downstream vendor failures. HCC submission pipelines are tied to CMS deadlines. Plans that have migrated EDW platforms mid-year have encountered submission delays, eligibility gaps, and STARS miscalculations that took quarters to unwind. Model the risk-adjusted cost of disruption before the procurement conversation starts.

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Skills Availability and Team Capability Gravity

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Databricks engineers are relatively scarce in healthcare and expensive to hire. Fabric's learning curve is shallower for teams with existing SQL, Power BI, and Azure skills  which describes most payer BI organizations. Data scientists, however, prefer Databricks' flexibility across Python, R, and ML libraries. The gravity of existing skill concentration often drives more adoption than any feature comparison. Retraining a Spark-native engineering team for Fabric adds cost and morale risk that never appears in a licensing proposal.

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CMS-0057-F Real-Time Data Exchange Obligations and Platform Readiness

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CMS-0057-F requires all payers to have FHIR-compliant APIs operational by January 1, 2027, covering patient access, provider directories, prior authorization, and payer-to-payer exchange. That requires sub-daily batch pipelines and low-latency reads against operational data. 

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Databricks' Structured Streaming is better positioned today for near-real-time pipeline architectures feeding FHIR API endpoints. Plans building these pipelines now should favor the platform with the more mature streaming track record.

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How PE-Backed Payers Should Weight Platform Decisions Against Exit Timelines

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PE-backed plans on a three to five year horizon have a version of this problem that strategic payers do not. A fragmented architecture with undocumented data flows is a valuation risk in due diligence. For shorter exit timelines, Fabric's turnkey BI delivery creates a cleaner narrative for acquirers. For longer horizons with active data science programs, Databricks pays off. What matters in either case is that the architecture is deliberate and documented, not a patchwork of decisions made under quarterly pressure.

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The Recommended Architecture Allocation Framework for Payer CTOs

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Transformation-Heavy Workloads: Risk Adjustment, Claims, Eligibility

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Use Databricks for 837/835 EDI ingestion, eligibility SCD management, EMPI resolution, HCC suspect scoring, and RAF pipeline orchestration. The Delta Lake engine, MLflow integration, autoscaling compute, and Unity Catalog governance make this the right home for workloads where transformation fidelity and ML maturity are non-negotiable.

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Analytics-Delivery-Heavy Workloads: STARS, MLR, Executive Reporting

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Use Fabric for STARS dashboards, MLR variance reporting, RAF trend views, and executive scorecards. Once data is clean and structured regardless of where it was processed Fabric's OneLake and semantic layer make BI delivery faster, more consistent, and easier to govern than a multi-tool stack.

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Where Mature Payer Architectures Run Both Platforms and How to Define the Boundary

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Mature payer architectures assign each workload to the platform it was built for. Databricks handles ingestion, transformation, and ML pipeline work. Fabric handles analytics delivery, semantic modeling, and BI distribution. That boundary must be a data contract, not a convenience. Tables produced in Databricks should be documented, versioned, and schema-governed before Fabric consumes them. Without that contract, running two platforms adds coordination overhead instead of removing it.

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How to Build the Internal Case for a Workload-Based Allocation Decision

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The argument to leadership is not "we need both platforms." The argument is: we are allocating each workload to the platform it was built for, reducing implementation risk on the pipelines with the highest compliance consequences. Document workload inventory, classify each workload as transformation-heavy or analytics-delivery-heavy, and map that to platform allocation. Show total cost of ownership including migration risk, skills investment, and the financial consequence of a CMS submission pipeline going wrong. That is a defensible narrative IT procurement, finance, and PE sponsors can all evaluate on their own terms.

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Final Thoughts

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The microsoft fabric versus databricks question is not about which platform is better. It is about which workloads belong where and whether an organization has the discipline to implement that allocation consistently. RAF accuracy, STARS reliability, and CMS compliance all depend on getting this right. Payer data environments are unforgiving with eligibility errors cascade, claims lag compounds, and HCC windows close. The platform decisions that look like infrastructure choices are revenue and compliance decisions in disguise. Treat them that way, and the allocation becomes straightforward.

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FAQs

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How can Invene help our organization navigate the Microsoft Fabric vs. Databricks decision?

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Invene is a healthcare-focused technology firm that specializes in building and implementing AI-driven data solutions for payers, providers, and healthtech companies. If your organization is weighing Microsoft Fabric against Databricks, Invene can assess your current architecture, data workflows, and strategic goals to recommend the right platform fit. We can also design a dual-platform approach where each tool handles the workloads it does best. With deep expertise in healthcare data standards, EMR integrations, population health analytics, and cloud infrastructure, Invene brings both the technical depth and domain knowledge needed to implement either platform in a compliant, high-performance environment. Our team has shipped products for 20 Fortune 100 clients, making them a proven partner for complex, high-stakes data initiatives in healthcare.

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Can a regional health plan run Microsoft Fabric without Databricks for its full data stack?

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Yes, but with tradeoffs. Fabric can handle ingestion, transformation, and BI delivery in one platform. The limitation is that transformation-heavy workloads  HCC scoring, EMPI resolution, eligibility SCD management are less mature in Fabric's engineering layer. Smaller plans with lower member volume may find Fabric sufficient. Larger plans with active risk adjustment ML programs will encounter gaps that require workarounds.

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Does Databricks support Power BI reporting natively, or does it require Fabric?

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Databricks supports Power BI through DirectQuery and partner connectors, but Power BI is not native to Databricks the way it is in Fabric. Teams using Power BI on Databricks typically face more friction in semantic model management, refresh scheduling, and report governance. If Power BI is a primary reporting tool and delivery speed matters, Fabric's native integration is a genuine operational advantage.

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How does the V24 to V28 HCC transition affect platform selection?

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The V28 transition requires updating ML features, retraining scoring models, and reprocessing historical submissions. All this transformation-layer work belongs in Databricks. V28 does not change the reporting layer. Plans going through this transition should expect the bulk of the effort to fall on Databricks engineering, not Fabric.

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What is the right platform for managing ADT feeds and hospital census data in a Medicare Advantage environment?

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ADT feeds from HIEs are near-real-time streaming data requiring low-latency ingestion and matching against an active member roster. Databricks' Structured Streaming handles this better than Fabric's current streaming tooling. Once census data is processed and matched, downstream STARS reporting on post-discharge follow-ups can be delivered through Fabric.

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How should a payer CTO present a dual-platform architecture to a PE sponsor concerned about cost?

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Frame it around risk reduction. Allocating transformation workloads to Databricks and analytics delivery to Fabric reduces implementation risk on the highest-consequence pipelines while shortening time-to-insight for the business stakeholders who drive valuation metrics. Quantify the cost of getting those pipelines wrong, such as CMS submission errors, eligibility reconciliation failures, delayed STARS reporting. Then compare that against the licensing delta. The math typically supports the dual-platform allocation.

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