Semarchy and Microsoft Fabric: Mastered Data in OneLake for AI-Ready Analytics

Semarchy’s latest push to embed mastered, governed data into Microsoft’s Fabric stack signals a pragmatic next step in the race to make enterprise data both AI-ready and business-ready — bringing golden records, semantic models, and DataOps workflows directly into OneLake so Power BI, GitHub Copilot, and other Fabric-native tools can consume trusted data at scale.

Background​

Semarchy is a long-standing player in master data management (MDM) and unified data platforms; over the past several years the company has deepened ties to Microsoft’s data ecosystem, extending integration with Microsoft Purview and now moving to a tighter interoperability with Microsoft Fabric and OneLake. Semarchy describes the integration as a way to publish mastered data products — datasets, APIs, metadata, and semantic models — so they are natively available to Fabric workloads and discoverable through Purview. Semarchy’s public materials and partner pages frame this as a DataOps-friendly approach to get certified “golden records” into the systems analysts and AI tools already use. (semarchy.com)
Meanwhile, Microsoft’s Fabric platform continues to evolve around OneLake (Fabric’s unified data lake) and the concept of semantic models — enterprise-friendly, reusable business models that sit over raw tables and represent metrics, hierarchies, and business terminology for analytics and AI consumption. Fabric has invested heavily in making semantic models exportable to Delta tables in OneLake and accessible across Fabric workloads like Power BI, Data Engineering, and the Fabric Data Warehouse. These capabilities are part of Microsoft’s deliberate effort to let the same canonical model back both human reports and programmatic AI workloads. (learn.microsoft.com)
FabCon — Microsoft’s Fabric community conference — has become the staging ground for many of these vendor integrations and product previews; the European FabCon Vienna schedule and Fabric community updates have highlighted OneLake, semantic models, and Copilot experiences as core themes for 2025. Semarchy’s announcement positions the integration as being demonstrated at FabCon Europe in Vienna as part of that industry rollout. (sharepointeurope.com)

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What Semarchy’s Fabric integration actually does​

Core capabilities​

• Semarchy can publish mastered data products (golden records, domain models, enriched datasets) so they are directly accessible from Microsoft Fabric workspaces and OneLake.
• The integration maps Semarchy metadata into Microsoft Purview, making lineage, certification status, and business context discoverable by Fabric users.
• Semarchy exposes enriched data and semantic models for consumption by Power BI reports, Fabric Data Warehouse workloads, Data Engineering processes, and real-time intelligence features, enabling a single source of truth for analytics and AI.
• DataOps workflows are enabled through familiar developer tooling: Visual Studio Code, Git integration, GitHub (including GitHub Copilot), and CI/CD practices so teams can produce, version, and publish data products rapidly.

These are not lightweight connector claims — Semarchy describes a deeper coupling where Semarchy-managed “golden” entities, combined with semantic models, are made available as Fabric-native artifacts so downstream consumers don’t have to re-master or revalidate the data before use. Semarchy also notes availability on cloud marketplaces (Azure, Snowflake, GCP, AWS) to simplify deployment. (semarchy.com)

How this fits into Fabric’s architecture​

At a technical level, the integration leverages two Fabric design patterns:

• Export or mirror the semantic model’s import tables into Delta tables in OneLake so other compute engines (Spark, SQL analytics, Data Warehouse) and services (Power BI) can read them directly.
• Surface associated metadata (entity definitions, lineage, certification status) into Purview so governance and discovery stay linked to the mastered artifacts.

Microsoft’s OneLake integration for semantic models already supports exporting import-mode tables to Delta format in OneLake, enabling direct access from multiple Fabric engines and improving interoperability across workloads. That facility is fundamental to Semarchy’s pitch: if mastered records can be reflected and synchronized into OneLake Delta artifacts, Power BI and Copilot experiences can query the same governed dataset without copy-and-paste transformations. (learn.microsoft.com)

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Why this matters: business and technical upside​

1) Shorter path from trusted data to insights​

Business users and analysts frequently distrust ad-hoc datasets. When MDM systems certify a single canonical view of business entities (customers, products, suppliers) and those certified records are made directly available in OneLake, the chain from transaction systems to dashboard shrinks. Reports built on top of a Semarchy-published semantic model carry the context and stewardship metadata that analysts need to trust metrics.

2) Better outcomes for AI and Copilot experiences​

Copilot-style assistants and large language models are highly sensitive to the quality and context of the data they query. If Copilot or Fabric’s AI features retrieve enriched, semantically labeled, and governed golden records (rather than raw, inconsistent tables), outputs — from narrative summaries to generated SQL — will be more accurate and less likely to propagate bad assumptions. Microsoft’s work to bring Copilot into Power BI and Fabric makes this integration timely: giving AI agents a single source of authorized, curated context materially reduces hallucination risk and accelerates answer fidelity. (microsoft.com)

3) DataOps at scale​

Semarchy’s emphasis on Git-based workflows, Visual Studio Code, and GitHub Copilot integration means data teams can manage data products with the same discipline used for application code. Version control, PRs, code reviews, and automated publishing reduce friction between data stewardship and analytics delivery. For companies that have struggled with fragmented pipelines, the combination of MDM governance plus GitOps practices in Fabric is a clear productivity lever. (semarchy.com)

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Technical deep dive: semantic models, Delta tables and OneLake​

Semantic models vs. raw tables​

Semantic models in Fabric are not just datasets; they embody business logic (measures, hierarchies, display names) and usage constraints (RLS, OLS). Microsoft’s OneLake integration allows semantic models in import mode to export their import tables into Delta format within OneLake; those Delta tables become first-class artifacts consumable by Spark, SQL, and other Fabric engines. In practice, this means a single semantic model can be published and then accessed either as a semantic layer (for Power BI) or as Delta tables (for programmatic workloads). (learn.microsoft.com)

Lineage and governance in Purview​

A critical piece of enterprise adoption is governance: who certified the record, when, and what transformations were applied. Semarchy’s integration claims to push key stewardship metadata into Microsoft Purview so data stewards and analysts can find mastered entities, inspect lineage, and see certification badges before using data in analysis. This closes a major governance loop that typically lives outside analytics tooling. Semarchy’s prior Purview integration work underpins this capability and makes the metadata sharing credible. (semarchy.com)

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Where Semarchy + Fabric will help first (use cases)​

• Customer 360: Publish canonical customer records into OneLake and overlay behavioral/transactional feeds with the same semantic model for consistent reporting and AI-driven personalization.
• Product data management: Master product hierarchies and attributes in Semarchy, expose them to Fabric for pricing analytics, inventory forecasting, and generative product descriptions by Copilot.
• Compliance reporting: Use Purview-discoverable certifications and lineage to reduce audit cycles and demonstrate governed metrics in regulatory reporting.
• Real-time analytics: Pair Semarchy’s gold records with Fabric’s Real-Time Intelligence to power operational dashboards that combine streaming events with certified master data.

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Strengths and notable positives​

• End-to-end alignment: Linking MDM, semantic models, OneLake Delta artifacts, and Purview closes the loop between governance and analytics — a practical requirement for enterprise-scale BI and GenAI.
• Developer-friendly DataOps: Semarchy’s emphasis on Git, VS Code, and Copilot integration lowers friction for engineering teams and institutionalizes reproducible data product delivery.
• Interoperability with Fabric features: Because Fabric already supports semantic model exports to OneLake and cross-workload consumption, Semarchy’s approach fits into Microsoft’s recommended architecture rather than trying to bolt in an external pattern. (learn.microsoft.com)
• Marketplace availability: Semarchy’s platform has previously been listed in cloud marketplaces, simplifying procurement and deployment for Azure-centric shops, which reduces procurement friction. (semarchy.com)

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Risks, limitations, and unanswered questions​

1) Operational complexity and sync semantics​

Exporting semantic models to Delta tables is powerful, but it introduces synchronization semantics that teams must manage. Exported Delta tables may have refresh cadence, retention windows for older versions, and transformation limits (for example, measures and certain calculated items won’t translate to raw Delta tables). Teams must plan refresh and consistency guarantees between the Semarchy-managed source of truth and the exported artifacts. Microsoft’s OneLake export tooling includes specific limitations and refresh behavior that architects must understand. (learn.microsoft.com)

2) Access controls and security model alignment​

OneLake and Fabric have evolving permission models (workspace roles, capacity-level settings, row/column security). Enterprises must ensure Semarchy’s governance semantics (certification, steward assignment) are synchronized with OneLake security policies and Purview access controls, otherwise trust signals in Purview could outpace runtime enforcement in Fabric workloads.

3) Vendor lock-in and semantic model portability​

Exposing semantic models as first-class entities inside OneLake is attractive, but organizations should evaluate portability: how tied are those semantic models to Fabric’s export formats, and what is the migration path if a customer needs to move to a different lake or analytics stack? Semantic model formats, XMLA behaviors, and delta table conventions differ across platforms; architects should require exportability and clear fallbacks.

4) Claim verification and demonstration footprint​

Semarchy’s announcement positions a demo at FabCon Europe in Vienna and describes “first of several integrations.” while public Semarchy materials confirm the strategic partnership and prior Purview integration, independent proof of specific FabCon sessions or hands-on availability of the feature in a general-availability form is limited in public documentation at this moment. Organizations should treat the integration as a supported preview/partner capability until they validate GA status and SLAs with Semarchy and Microsoft. (semarchy.com)

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Implementation checklist for architects and data leaders​

• Inventory current master data sources and stewarding processes; identify candidate domains (customer, product, supplier) where a canonical record will materially improve reporting.
• Validate Fabric capacity and Power BI SKU prerequisites: OneLake integration for semantic models and Direct Lake features may require specific Fabric/Power BI SKUs and workspace settings.
• Prototype a single domain: publish Semarchy’s mastered dataset to OneLake, export the semantic model to Delta, and build a Power BI report and a Copilot prompt flow that references the exported artifact.
• Configure Purview synchronization: ensure Semarchy’s metadata flows into Purview and that lineage and certification are visible to data consumers.
• Automate DataOps: set up Git integration, CI/CD pipelines, and guardrails for semantic model edits, and implement role-based approvals for publishing changes.
• Validate security posture: test row/column security propagation, and ensure that OneLake permission enforcement matches governance expectations.
• Measure and iterate: instrument query performance, refresh latencies, and Copilot accuracy metrics to quantify the uplift from moving to mastered, semantically surfaced data.

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Competitive context and what vendors are doing​

Semarchy is not alone in pushing MDM into Fabric. Other enterprise data-management vendors (including large incumbents) have announced Fabric integrations or native Fabric apps that bring data quality, lineage, and MDM-like features into OneLake and Fabric’s workloads. Informatica, for example, publicized MDM and data-quality extensions into Fabric earlier in 2025. This broader vendor movement validates Microsoft’s strategy: Fabric is rapidly becoming the converged place where governance, engineering, analytics, and AI meet. For buyers, the choice now hinges on integration depth, operational model, and whether the vendor’s approach is native (in-lake) or adjacent (external service that pushes artifacts). (informatica.com)

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Recommendations for enterprises evaluating the Semarchy-Fabric route​

• Treat the Semarchy-Fabric integration as a strategic enabler for trusted analytics and AI, but require a proof-of-concept that demonstrates end-to-end DataOps, governance, and runtime enforcement.
• Prioritize domains where the value of a canonical record is measurable (e.g., revenue, regulatory reporting, customer acquisition cost) and pilot with a two-quarter roadmap.
• Validate operational contracts: refresh windows, retention of older Delta versions, and expected latency between Semarchy changes and Fabric consumers.
• Confirm Purview sync semantics and whether key stewardship metadata (certifications, steward owners, SLA attributes) is searchable and actionable for downstream users.
• Get legal and procurement teams to align on marketplace licensing vs. managed service SLAs if you plan to deploy via Azure Marketplace.

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Final assessment​

Semarchy’s announcement is a pragmatic, well-timed extension of the long-standing trend to bring data governance and MDM closer to analytics and AI runtimes. By focusing on OneLake and Fabric semantic models, the integration promises to reduce friction between master-data certification and day-to-day analytics consumption, while enabling DataOps practices that many organizations are still struggling to adopt.
However, the integration is not a silver bullet. Successful adoption will depend on careful orchestration of sync semantics, enforcement of security and access control across layers, and disciplined governance practices that keep the “single source of truth” actually synchronized and trusted by end users. Organizations evaluating this approach should insist on a hands-on pilot that proves both technical feasibility and measurable business outcomes before broad rollout.
For organizations with an existing Microsoft Fabric investment and a need to operationalize trusted master records for analytics and AI, Semarchy’s Fabric integration is a compelling option — provided teams validate refresh guarantees, governance enforcement, and the operational model in their own environment before making long-term commitments. (learn.microsoft.com)

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Semarchy’s move highlights a central truth about modern analytics: governance and agility must coexist. The companies that get both right — marrying certified golden records with developer-friendly DataOps and Fabric’s semantic surface area — will have a decisive advantage in delivering reliable analytics and trustworthy AI at enterprise scale.

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Source: Business Wire https://www.businesswire.com/news/home/20250915943713/en/Semarchy-Announces-Microsoft-Fabric-Integration-to-Power-Trusted-Data-Insights-in-Microsoft-Power-BI-and-GitHub-Copilot/

 

[ChatGPT]

Semarchy’s new integration with Microsoft Fabric delivers a practical bridge between enterprise-grade master data management and the Fabric analytics and AI stack, publishing Semarchy’s enriched “golden” records and semantic models into Microsoft OneLake so Power BI, Fabric services, and GitHub Copilot can consume governed, production-ready data at scale.

Background / Overview​

Semarchy, a long-standing player in Master Data Management (MDM), announced a deeper integration between the Semarchy Data Platform and Microsoft Fabric that makes mastered data products — datasets, APIs, metadata and semantic models — natively available inside Fabric workspaces and OneLake. The vendor positions this as a DataOps-driven way to shorten the path from trusted data to analytics and AI outcomes, with metadata surfaced into Microsoft Purview for discovery and governance. (semarchy.com)
Microsoft Fabric’s architecture centers on OneLake (the platform’s logical lakehouse), semantic models that represent business logic and metrics, and cross-workload access to Delta-formatted tables. Fabric already supports exporting import-mode semantic model tables to Delta tables in OneLake so other engines (Spark, SQL, Data Warehouse, and Power BI) can read them directly — a capability the Semarchy integration leverages to expose golden records where analysts and AI agents will find them. (learn.microsoft.com)

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What the integration actually delivers​

Core capabilities (what Semarchy claims)​

• Publish mastered data products (golden records, domain models, enriched datasets) directly into OneLake where Fabric workloads can consume them.
• Export or mirror semantic model import tables as Delta tables in OneLake so Power BI, Data Engineering, the Fabric Data Warehouse and downstream compute engines can read the same authoritative artifacts. (learn.microsoft.com)
• Surface stewardship metadata into Microsoft Purview so lineage, certification status, and business context are discoverable by analysts and governed tools. (semarchy.com)
• Enable DataOps workflows using familiar developer tooling: Visual Studio Code, Git/GitHub integrations, GitHub Copilot-assisted authoring, and CI/CD pipelines to version and publish data products.

These features are aimed at making master data not only technically available inside Fabric, but also usable in a controlled, reproducible way that matches how modern data engineering teams operate.

What’s new vs. previous Semarchy integrations​

Semarchy has previously integrated with Microsoft Purview and other Azure services; this announcement extends that partnership by concentrating on Fabric-native artifacts (OneLake Delta exports and semantic models) to reduce manual steps analysts historically used to re-master or reconcile datasets. The integration’s novelty is the explicit coupling of Semarchy-sanctioned golden records with Fabric’s semantic model export and Purview discoverability. (semarchy.com)

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Technical anatomy: how the pieces fit​

OneLake and Delta exports​

Fabric’s OneLake integration allows semantic models in import mode to export import tables to Delta format in OneLake. When Semarchy publishes a mastered dataset and its semantic definitions, the integration maps those artifacts into Fabric so they can be exported as Delta tables and consumed across engines and tools. That means the same canonical record used to certify metrics can be queried by T-SQL, Spark, or read directly by Power BI reports with minimal duplication. (learn.microsoft.com)
Key technical details verified in Microsoft’s documentation include:

• OneLake integration for semantic models is supported on Power BI Premium P and Microsoft Fabric F SKUs (not available on Pro or Premium Per User). (learn.microsoft.com)
• Not all semantic model artifacts are exportable — DirectQuery tables, measures, calculation groups and some system-managed objects are excluded from export to Delta. Old Delta table versions are retained only for a short window (documented behavior) and other limitations apply. These constraints must inform any architecture that expects complete fidelity between the semantic model and OneLake artifacts. (learn.microsoft.com)

Purview metadata flow​

Semarchy’s approach maps stewardship metadata (certifications, owner, lineage, SLA tags) into Microsoft Purview so governed consumers can discover which golden entities exist, who certified them, and how they were derived. This closes a common governance loop where MDM lives in a separate control plane from analytics. Semarchy’s prior Purview partnership work confirms the feasibility of this sync. (semarchy.com)

DataOps and developer tooling​

The integration emphasizes Git-based DataOps:

• Version control of semantic model and data product definitions via Git/GitHub
• Authoring and automation through Visual Studio Code and CI/CD pipelines
• Assisted development and documentation using GitHub Copilot integrations

This creates a reproducible pipeline for data product lifecycle management that mirrors modern software engineering practices, helping reduce drift and improve traceability for analytics outputs.

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Business value: what organizations stand to gain​

Faster, trustworthy insights​

By publishing certified golden records directly into OneLake and making them discoverable in Purview, organizations can shorten the analytic path from transactional systems to dashboards. Analysts and business users get metrics backed by stewardship metadata, which helps reduce disputes over “whose numbers are correct” and accelerates decision-making.

Better AI and Copilot outcomes​

Copilot-style assistants and generative AI consume and reason over context. When those agents query enriched, semantically labeled, and governed golden records — instead of ad-hoc, inconsistently cleaned tables — their outputs (narratives, generated SQL, summaries) are more reliable and auditable. The integration is explicitly positioned to improve the fidelity of AI-driven insights inside Power BI and Fabric.

DataOps productivity and governance alignment​

Embedding MDM into DataOps pipelines and enabling Git-based lifecycle management helps institutionalize code review, branching, and automated publishing for data products. This reduces operational friction between data stewards and analytics teams and makes governance checks an integral part of releases.

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Practical implementation checklist (for architects and data leaders)​

• Inventory candidate domains (customer, product, supplier) and map current source-of-truth systems to Semarchy-managed entities.
• Validate Fabric and Power BI entitlements: confirm you have the required Power BI Premium P / Fabric F SKUs for OneLake semantic export. (learn.microsoft.com)
• Prototype flow: publish a mastered dataset from Semarchy to OneLake, export semantic model tables to Delta, and build a Power BI report + Copilot prompt that reads the exported artifacts.
• Configure Purview sync and verify lineage and certification metadata is searchable and visible to analysts. (semarchy.com)
• Automate DataOps: enable Git integration, CI/CD, and guardrails for publishing semantic model changes. Add code-review gates and define SLAs for publishing.
• Test security posture: confirm row-level security and permissions propagate correctly from Fabric semantic models to exported Delta artifacts and that OneLake/Power BI access control meets compliance needs. (learn.microsoft.com)

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Strengths and notable positives​

• End-to-end alignment between MDM and analytics — Closing the loop between certified master records and downstream analytics reduces data reconciliation effort and aligns AI agents to governed truth.
• Leverages Fabric-native capabilities — The approach uses Microsoft’s documented OneLake export and semantic model behavior rather than brittle, external copy strategies. This improves interoperability across Spark, SQL and Power BI. (learn.microsoft.com)
• Developer-focused DataOps — Built-in Git, VS Code, and Copilot integrations help teams adopt versioned, repeatable workflows for data product lifecycle.
• Governance visibility — Pushing stewardship metadata into Purview helps analysts find and trust datasets before they use them. (semarchy.com)

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Risks, caveats, and implementation pitfalls​

Licensing and SKU constraints​

OneLake integration for semantic models is not universally available — it requires specific Power BI / Fabric SKUs (Premium P and Fabric F). Organizations may face unexpected licensing or cost implications if they attempt to extend OneLake exports across many domains without planning capacity and SKU entitlements. Confirm tenant-specific entitlements before design decisions. (learn.microsoft.com)

Export limitations and fidelity gaps​

Not every semantic-model artifact maps perfectly to Delta exports. Complex constructs such as measures, calculation groups, and DirectQuery/hybrid tables may not export. Organizations that expect a byte-for-byte parity between the semantic model and exported Delta artifacts will need to design around these limitations and consider how to represent complex business logic in exportable forms. (learn.microsoft.com)

Latency and freshness trade-offs​

Publishing mastered records into OneLake and exporting Delta artifacts introduces synchronization windows. Teams must define and validate refresh SLAs (how quickly changes in Semarchy propagate to Delta exports and into Power BI/Copilot). For real-time or sub-second operational scenarios, consider whether Fabric’s “near-real-time” semantics meet business requirements. (learn.microsoft.com)

Governance and security enforcement across layers​

Making authoritative data discoverable is powerful, but it increases the attack surface if access controls and sensitivity labeling are not enforced consistently. Ensure that Purview metadata, OneLake permissions, and Power BI row-level security align and that sensitive attributes are protected end-to-end. Auditability and logging must be validated during pilots.

Cost of scale and operational overhead​

Delta storage, export operations, compute for model refreshes, and Copilot/AI consumption all carry costs. Measure and model both storage/versioning needs and the compute chargebacks for refresh cadence and Copilot query volumes to avoid surprising bills. Pilot groups are essential to gather realistic cost telemetry.

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Recommended rollout approach​

• Start with a focused pilot on a high-value domain (Customer 360 or Product Master) where canonical records will materially reduce reconciliation overhead. Measure time-to-insight improvements and AI accuracy uplift in this closed loop.
• Validate end-to-end SLAs: publish a change in Semarchy, measure the latency until the Delta export is refreshed in OneLake and until Power BI/Copilot reflects the change. Document edge cases and data-quality guards. (learn.microsoft.com)
• Harden governance: configure Purview metadata, sensitivity labels, and test RLS propagation. Conduct threat modelling for Copilot scenarios that surface governed records. (semarchy.com)
• Automate and codify deployment: use Git/GitHub CI/CD for semantic model changes, require PR review for publishing, and automate automated tests that validate lineage and schema expectations.

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Where the integration matters first (use cases)​

• Customer 360 and personalization — A single canonical customer record reduces personalized campaign errors and ensures analytics and AI personalization use the same identifiers and attributes.
• Finance and compliance reporting — Governed golden ledgers and certified dimensions reduce audit cycles and provide traceable lineage for regulatory reporting.
• Product information management and pricing analytics — Mastered product hierarchies and attributes published to OneLake enable consistent pricing models, inventory forecasting, and Copilot-augmented product descriptions.
• Operational dashboards / near-real-time intelligence — Combining certified golden records with Fabric’s real-time intelligence facilities can power operational dashboards that require both accurate master data and event streams. Validate latency needs before committing.

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Verification, cross-references, and cautionary flags​

Key technical claims in this analysis have been cross-checked against Microsoft Fabric documentation on OneLake integration and semantic models as well as Semarchy’s product and partnership announcements. Microsoft confirms OneLake integration behavior, SKU prerequisites, and export limitations in its Learn articles. Semarchy’s public materials and press statements confirm the Purview metadata sync and the DataOps tooling emphasis. Readers should treat claims around future product roadmaps, timing for additional integrations, and vendor roadmap promises as evolving and verify tenant-specific entitlements and timelines in their Microsoft admin portals and Semarchy account teams before procurement decisions. (learn.microsoft.com)
Unverifiable or rapidly changing claims — such as exactly which Copilot model versions will be used for any specific tenant workload, or precise future pricing/entitlement changes — should be treated as subject to change and checked with vendor contracts and Microsoft communications.

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Bottom line — pragmatic take for WindowsForum readers​

Semarchy’s Fabric integration is a pragmatic advance: it aligns enterprise master data with Fabric’s semantic model-first architecture and OneLake’s Delta-formatted artifacts, while embedding governance via Purview and operationalizing delivery through DataOps tooling. For organizations already invested in Microsoft Fabric, this reduces barriers to adopting trusted data across analytics and AI. However, it is not a turnkey cure for governance and cost challenges; success depends on thoughtfully designed pilots, clear SLA measurements for refresh and propagation, and disciplined enforcement of security and lifecycle controls.
Enterprises should approach the integration as a strategic enabler — one that reduces friction between MDM and analytics but requires careful planning around licensing, export limitations, latency expectations, and governance enforcement. A short, measurable pilot on a high-value domain is the correct first step: demonstrate value, quantify costs, validate refresh semantics, and then scale with DataOps controls in place. (learn.microsoft.com)

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Semarchy is demonstrating this integration publicly at Fabric community events (including FabCon EMEA demos noted by the vendor), and the integration is presented as the first of several planned steps to deepen Fabric support — a roadmap buyers should verify with their Semarchy and Microsoft contacts before committing to a broad rollout.
Conclusion: the Semarchy–Microsoft Fabric pairing offers a clear, architecturally consistent route to put trusted master data into the same runtime that powers modern BI and AI. When implemented with attention to SKU entitlements, export limitations, and governance, it can materially shorten the path from certified records to actionable, auditable insights.

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Source: 01net Semarchy Announces Microsoft Fabric Integration to Power Trusted Data Insights in Microsoft Power BI and GitHub Copilot