A unified analytics platform that combines Power BI's visualization with Microsoft Fabric's lakehouse, semantic model, and data engineering layers — built so finance, ops, and analysts work from the same numbers.
Most Power BI estates start as a couple of helpful reports and grow into a quiet sprawl. Different teams pull from different sources, write similar measures slightly differently, and arrive at meetings with conflicting numbers. Fabric is the chance to fix the foundation without re-platforming.
Dozens of workspaces, hundreds of reports, and no shared semantic layer. Sales pulls CRM exports, finance keeps weekly extracts, ops connects directly to production.
Gateway-bound imports and overnight batches mean the dashboard people open at 9am is already a day behind the question being asked.
OneLake is on. A lakehouse exists. Nobody is sure which workspace owns the gold layer, what Direct Lake actually buys you, or how to govern it.
Eight focused capabilities, picked off the shelf to match where you are. We don't sell the whole catalog — we sequence it.
Star schemas, governed measures, conformed dimensions. The DAX layer everyone else builds on top of.
Bronze · Silver · Gold layers in OneLake, governed shortcuts, sensible workspace boundaries.
Sub-second reports against multi-billion-row models — without copying data into the semantic layer.
Power Query at scale, orchestrated with Fabric pipelines and notebooks where SQL or Spark earns its place.
Layout, accessibility, and DAX patterns that make the report feel as considered as the model behind it.
Row-level security, object-level security, and Microsoft Purview labels wired into the semantic model.
Copilot in Power BI, custom Q&A linguistic schemas, and embedded reports inside the apps your team already uses.
Source-controlled models with Tabular Editor, Git integration, and deployment pipelines from dev to prod.
How the pieces fit on a typical Arkimetrix engagement. We don't insist on every layer — we recommend the smallest stack that answers the questions you actually ask.
Five phases, each with a single deliverable a stakeholder can sign off. No 80-page strategy decks.
Tenant audit, workspace map, and a shortlist of the reports actually used.
Target architecture, model design, governance pattern. One readable diagram.
Lakehouse, semantic model, three foundational reports — sliced by adoption risk.
Retire the duplicates. Remap legacy reports onto the governed model.
Train your analysts on the model. Document the patterns. Hand the keys back.
Outcomes worth aiming for — what we've seen consistently after a focused Power BI & Fabric engagement. Numbers will vary by tenant; we'll baseline yours during discovery.
A single governed model. The same "revenue" measure across finance, ops, and product reports.
Direct Lake against OneLake means sub-second response on multi-billion-row tables — without scheduled refresh windows.
Analysts compose new reports against approved models, with RLS and sensitivity labels applied by default.
Right-sized Fabric capacity replaces a sprawl of Pro licenses, gateways, and ad-hoc Azure resources.
Source-controlled models, deployment pipelines, and a documentation pattern your team can extend.
A four-to-six-week engagement that lands a governed semantic model, one production report, and a Fabric architecture you can build the rest of the year on.
Engagements scoped per tenant. Fixed-price options available for foundations work; T&M for build-out.
The questions we get most often during scoping calls. If yours isn't here, write to info@arkimetrix.com.
No. We work across the full licensing spectrum — Power BI Pro, Premium Per User (PPU), standalone Power BI Premium capacity tenants, and Fabric-enabled tenants. If you're on Pro or PPU today, we'll help you get the most out of what you already pay for before recommending an upgrade. If you're on Premium, we'll help you decide whether and when turning on the Fabric tenant setting makes sense — and what to do first when you do.
Direct Lake reads Delta Parquet files directly from OneLake into the Power BI semantic model — no scheduled import, no query pass-through to a source database. The result is import-mode performance with live data freshness, on multi-billion-row tables. Trade-offs apply (calculated columns, cross-source models), and we'll walk you through them in discovery.
Most are worth keeping. We catalogue what's actually used, retire the duplicates, and remap the survivors onto a governed semantic model. The user-facing experience usually improves; the back-end becomes maintainable.
Yes — most of our engagements are co-builds. We bring patterns, conventions, and a senior pair-programmer for your analysts. By the end of the engagement, your team owns the model and the documentation.
Toronto, Canada and Pune, India. One team, two time zones. Most clients see this as a feature, not a bug — coverage runs nearly around the clock.
A 30-minute scoping call. We'll look at your tenant, your top three reports, and the question your CFO keeps asking. We'll tell you whether we're a fit — and what we'd do first.