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

Microsoft Fabric is a genuine step forward and a genuine way to spend money quickly, and which one you get depends almost entirely on decisions made in the first fortnight.

This page is about building a data platform for an organisation in Zurich, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Zurich's base in retail and commercial banking, asset and wealth management, 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 Switzerland's principal banking and insurance centre and one of Europe's leading technical universities.

Does a Zurich 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 Zurich

Asset and wealth management

Position, transaction and valuation data arriving from custodians and portfolio systems on different cycles, with market data licensed under terms that restrict redistribution. Source systems typically include portfolio management system, custodian files, order management system, market data feeds and CRM.

For a platform design, the consequence is specific. Reporting built on the custodian feed and reporting built on the portfolio system disagree, and nobody owns the reconciliation between them. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a position and valuation model with an explicit as-at dimension, so every figure states the valuation date it was computed on.

Retail and commercial banking

High-volume transactional data at account and posting grain, with a core banking system that was never designed to be queried analytically and a general ledger that is authoritative but coarse. Each function builds its own extract from core banking with its own as-at timing, and the resulting small variances are never reconciled. They are simply explained away each month.

Systems to plan for: core banking platform, general ledger, card and payment processors, credit decisioning systems and CRM. Account-level data is both personal data and commercially sensitive, so row-level security and export control are design requirements rather than later hardening.

Software and technology

Product event streams at very high volume alongside subscription, billing and support data that must be joined at account grain to be useful. ARR comes from a finance spreadsheet with manual adjustments, so restating a prior period requires archaeology and diligence becomes painful. Product telemetry on identifiable users needs a lawful basis and consent handling, and SOC 2 or ISO 27001 evidence often covers the analytics estate.

A medallion layout for an asset and wealth management 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 Zurich client in asset and wealth management.

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

With Direct Lake the model reads Delta directly from OneLake, so there is no refresh to schedule and no query round trip. The catch is the condition attached: anything unsupported falls back, and you are then on SQL endpoint performance without having chosen it. 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 asset and wealth management workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Zurich 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 portfolio management system.

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 Zurich specifically, Switzerland North (Zurich) is the normal primary with Switzerland West (Geneva) as the pair. Latency from Zurich 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 Zurich 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: Zug, Lugano, Geneva, Lausanne, Lucerne and Winterthur.

Frequently asked questions