Microsoft Fabric and Azure data platforms in St. Gallen: 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 St. Gallen organisations find themselves in when they call us.

This page is about building a data platform for an organisation in St. Gallen, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. St. Gallen's base in professional and legal services, textiles, fashion and luxury goods, and insurance and reinsurance 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 a business university with an international reputation and textile industry heritage with surviving high-end producers.

Does a St. Gallen organisation actually need Fabric?

Fabric starts to earn its keep when several of the following are true at once: volumes that strain an import model, a genuine need for both SQL and notebook access to the same data, several teams requiring governed self-service, retention obligations that make a desktop model impossible, or an existing Premium capacity. 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 St. Gallen

Textiles, fashion and luxury goods

Short product lifecycles with size and colour variants multiplying SKU counts, plus wholesale, retail and online channels running on different calendars. Source systems typically include PLM, ERP, wholesale order management, retail EPOS and ecommerce platform.

For a platform design, the consequence is specific. Reporting at style level only, which hides that one size curve or colourway is carrying all the markdown risk. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be an option-grain sales and stock model with season and markdown dimensions, which is where buying and merchandising decisions are actually made.

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.

Systems to plan for: practice management system, time recording, professional services automation, CRM and finance system. Matter confidentiality and information barriers must be inherited by analytics through row-level security, not re-implemented loosely.

Insurance and reinsurance

Policy, premium and claims data where the same risk is represented differently in underwriting, finance and claims systems, and where development over time is the whole point. Actuarial results land as spreadsheets with no lineage, so the reported reserve movement cannot be tied back to the model run that produced it. Claims data frequently includes health and criminal-conviction information, which attracts the stricter GDPR special-category conditions.

A medallion layout for a textiles, fashion and luxury goods 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 St. Gallen client in textiles, fashion and luxury goods.

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.

Capacity is sized from measurement, not from a spreadsheet. Each workload is profiled for its compute shape, for background refresh against interactive query, for concurrency and for when the peaks actually fall, then the result is checked on a trial capacity running your own data. 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 textiles, fashion and luxury goods workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few St. Gallen 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 PLM.

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 St. Gallen specifically, Switzerland North (Zurich) is the normal primary with Switzerland West (Geneva) as the pair. Latency from St. Gallen 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 St. Gallen 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, Baden, Aarau, Zurich and Bern.

Frequently asked questions