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

A Fabric capacity bought before anyone has profiled the workloads is an expense in search of a benefit. That is the position several Baden organisations find themselves in when they call us.

This page is about building a data platform for an organisation in Baden, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Baden's base in energy generation and supply, industrial machinery and engineering, and software and technology 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 power and energy technology engineering and industrial automation.

Does a Baden organisation actually need Fabric?

The case for Fabric gets strong when more than one of these holds: import models under strain, the same data needed through both SQL and notebooks, governed self-service for several teams, retention that a desktop model cannot carry, or a Premium capacity already on the bill. 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 Baden

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. Source systems typically include ERP, PLM / CAD, project management, field service management and CRM.

For a platform design, the consequence is specific. Project margin is only known at completion, so loss-making contracts are discovered when it is far too late to act. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a project cost and margin model with an installed-base dimension, which turns aftermarket service from an anecdote into a managed revenue line.

Energy generation and supply

Half-hourly or sub-hourly interval data at national scale, settlement runs that are revised months later, and metering that is partly estimated. Later settlement runs overwrite earlier ones, destroying the ability to explain why a previously reported figure has changed.

Systems to plan for: meter data management, settlement systems, SCADA, trading systems and billing platforms. Market reporting obligations and NIS2 critical-infrastructure duties apply, and consumption data is personal data for domestic customers.

Professional and legal services

Time, billing and pipeline data where almost every meaningful figure is an allocation, and where confidentiality obligations constrain who may see matter-level detail. Four definitions of utilisation circulate simultaneously, so partner behaviour responds to whichever figure appears in the meeting they attend. Matter confidentiality and information barriers must be inherited by analytics through row-level security, not re-implemented loosely.

A medallion layout for an industrial machinery and engineering 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 Baden client in industrial machinery and engineering.

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.

Sizing comes from a trial capacity running your real workloads, profiled for compute shape, refresh against query load, concurrency and peak hours. Estimating it from user counts is how organisations end up throttled at month end. 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

The appeal of Direct Lake is that nothing is copied and nothing is refreshed. The risk is the fallback: one unsupported construct and the model quietly behaves like DirectQuery instead. 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 industrial machinery and engineering workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Baden 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 keep the old platform running until each domain is proven. The alternative, moving everything at once, is how migrations end up reversed. 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 Baden specifically, Switzerland North (Zurich) is the normal primary with Switzerland West (Geneva) as the pair. Latency from Baden 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 Baden 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: Lucerne, Winterthur, St. Gallen, Biel/Bienne, Yverdon-les-Bains and Rapperswil-Jona.

Frequently asked questions