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

The question we are asked most often by Wiesbaden organisations is whether they need Microsoft Fabric. The answer is frequently no, and saying so has cost us work we did not want.

This page is about building a data platform for an organisation in Wiesbaden, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Wiesbaden's base in public sector, professional and legal services, and chemicals and process industry 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 the federal statistical office and Land ministries and professional services and financial advisory firms.

Does a Wiesbaden organisation actually need Fabric?

No single condition justifies a capacity. Several together do: data that no longer fits an import model, a requirement for both SQL and notebook access, multiple teams needing governed self-service, retention obligations beyond what a desktop file can hold, or a Premium capacity you already pay for. 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 Wiesbaden

Public sector

Case and service data locked inside line-of-business systems with restrictive reporting modules, feeding statutory returns with precisely defined rules. Source systems typically include case management systems, finance and ERP, HR and payroll, GIS and gazetteers and grant management.

For a platform design, the consequence is specific. Returns are assembled by hand each cycle by a small number of experienced staff, concentrating risk in individuals. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a governed model that produces both the statutory return and internal performance reporting, so published and internal figures cannot diverge.

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.

Systems to plan for: policy administration, claims management, actuarial reserving models, reinsurance systems and broker feeds. Claims data frequently includes health and criminal-conviction information, which attracts the stricter GDPR special-category conditions.

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 a public sector 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 Wiesbaden client in public sector.

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 public sector workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Wiesbaden 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. Neither requires switching everything at once, which is the approach behind most of the failed migrations we are asked to rescue. The reference pipeline we build first is usually the system that hurts most, which here is normally case management systems.

Residency, latency and the Germany question

Germany West Central in Frankfurt is the usual choice and gives in-country residency with very low latency to the financial sector. Germany North provides a paired region. Residency alone does not satisfy a German works council or data protection officer. They will also ask about processor chains and access by support personnel, so those answers need preparing.

For Wiesbaden specifically, Germany West Central (Frankfurt) is the normal primary with Germany North (Berlin) as the pair. Latency from Wiesbaden 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.

• Betriebsrat co-determination is the single most underestimated factor in German analytics projects: any reporting that could be used to assess individual employee performance generally needs a works agreement before launch.
• MaRisk and BAIT expectations mean financial-sector data lineage and documentation standards are explicit, not inferred.
• The Lieferkettensorgfaltspflichtengesetz requires supply-chain due-diligence data that most ERP estates do not hold in reportable form.

Where to go next

If you are weighing a platform decision for a Wiesbaden 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 Germany and deliver into neighbouring markets regularly. Nearby pages worth reading if your group has sites elsewhere: Berlin, Cologne, Munich, Offenbach am Main, Mannheim and Frankfurt am Main.

Frequently asked questions