Microsoft Fabric and Azure data platforms in Solothurn: architecture, capacity and cost

Before deciding on a lakehouse, it is worth establishing whether your data actually outgrew an import model or simply outgrew the person maintaining it.

This page is about building a data platform for an organisation in Solothurn, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Solothurn's base in precision engineering and instruments, medical devices, and public sector determines the data volumes, the latency requirements and the retention obligations, and those three things decide the architecture. The client population here is shaped by precision engineering and medical device manufacturing and cantonal administration.

Does a Solothurn organisation actually need Fabric?

Look for a cluster rather than a single trigger: import refreshes that are getting uncomfortable, demand for SQL and notebooks against one copy, self-service across more than one team, retention that rules out a desktop model, or an existing Premium commitment. One analyst maintaining three import models that refresh in twenty minutes does not need a capacity, and buying one will not improve anything.

What the data actually looks like in Solothurn

Medical devices

Device history records, complaint and vigilance data, and post-market surveillance obligations that require linking field events back to manufacturing batches. Source systems typically include ERP, QMS, complaint handling systems, PLM and distributor reporting.

For a platform design, the consequence is specific. Distributor-held data breaks traceability at exactly the point where a field action needs it. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a traceability and complaint model linking batch, shipment, customer and field event, which is the foundation of post-market surveillance reporting.

Industrial machinery and engineering

Project and configuration data as much as production data: engineered-to-order items, long lead times, and service revenue that outlives the original sale. Project margin is only known at completion, so loss-making contracts are discovered when it is far too late to act.

Systems to plan for: ERP, PLM / CAD, project management, field service management and CRM. Export control and dual-use classification data should be modelled explicitly where machinery is shipped outside the EU and UK.

Tourism, hospitality and events

Booking, channel and rate data with heavy seasonality, plus on-site spend and occupancy that only reconcile once channel commission is accounted for. Gross booking value from online travel agents is compared with direct booking revenue, making the most expensive channel look like the best one. Guest data is personal data with cross-border transfer implications when group systems sit outside the region.

A medallion layout for a medical devices business

The medallion pattern is not complicated, and it is routinely implemented badly: a bronze layer that has already lost fidelity, a silver layer with no testable rules, and a gold layer that is really just the silver layer with different names. Here is how we would lay it out for a Solothurn client in medical devices.

Capacity sizing and what it really costs

Fabric capacity is billed by capacity unit. An F2 is roughly US$0.36 per hour pay-as-you-go, about US$260 a month, with around 40 per cent off for a one-year reservation. An F64, the smallest SKU at which report consumers no longer need an individual Pro licence, is roughly US$8,400 a month pay-as-you-go or around US$5,000 reserved.

The arithmetic that matters: F64 only becomes cheaper than per-user licensing somewhere in the region of 350 to 600 readers, depending on the discount you actually pay and whether you reserve capacity. Below that, Pro or PPU per user is normally the right answer, and buying capacity for prestige is a straightforward waste of money.

The sizing exercise is empirical: profile each workload, separate background refresh from interactive query, count real concurrency, find the true peak, then prove the number on a short-lived capacity before anything is bought. The output is a sizing model that states expected utilisation at launch, at double the readers, and the specific point at which you would need the next SKU. Always price from the current Microsoft rate card for your region and agreement. Any figure in an article older than a quarter should be treated as an order of magnitude, not a quote.

Direct Lake, import or DirectQuery

Direct Lake removes both the import refresh and the DirectQuery hop by reading Delta in place. It also carries a condition most teams discover late: unsupported constructs silently fall back, and the model then performs like DirectQuery. We test it against an import model on your own data and choose on the measured result. In practice Direct Lake tends to win on large, frequently landing tables, which medical devices workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Solothurn organisations start from nothing. The realistic options are a serving-layer-first adoption, where Fabric takes the gold layer and reporting while existing engineering stays in place, or a domain-by-domain migration with the old platform retained until each domain is validated. Both avoid a big-bang cutover, which is the pattern that produces the rollbacks we get called in to repair. The reference pipeline we build first is usually the system that hurts most, which here is normally ERP.

Residency, latency and the Switzerland question

Switzerland North and Switzerland West give in-country residency, which matters more here than almost anywhere else in Europe: banking secrecy, professional secrecy obligations and client expectation all push Swiss data to stay Swiss. Verify per service, because not every Azure or Fabric feature is available in both Swiss regions.

For Solothurn specifically, Switzerland North (Zurich) is the normal primary with Switzerland West (Geneva) as the pair. Latency from Solothurn to that region is not usually a constraint for analytical workloads; the constraints that bite are feature availability per region and the processor chain your data protection officer will ask about.

• The revFADP brought Swiss law closer to the GDPR but not identical to it: records of processing, impact assessments and breach notification follow Swiss rules, and an EU-shaped compliance pack needs adapting rather than copying.
• FINMA outsourcing expectations require documented control over cloud providers, including the right to audit and a tested exit plan.
• Article 321 of the Criminal Code and sectoral secrecy duties restrict who may see client-identifying data, which has direct consequences for row-level security design.

Where to go next

If you are weighing a platform decision for a Solothurn organisation, a workload assessment will answer it with evidence in two to three weeks, and the written recommendation is yours whatever you decide. Book a technical review.

We work across Switzerland and deliver into neighbouring markets regularly. Nearby pages worth reading if your group has sites elsewhere: Thun, Fribourg, La Chaux-de-Fonds, Yverdon-les-Bains, Rapperswil-Jona and Geneva.

Frequently asked questions