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

The question we are asked most often by Tübingen 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 Tübingen, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Tübingen's base in higher education and research, pharmaceuticals and biotech, and healthcare provision 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 research university with strong medicine and AI research and biotechnology and immunotherapy companies.

Does a Tübingen 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 Tübingen

Higher education and research

Student lifecycle, research grant and estates data in separate systems, with statutory returns and league-table submissions on fixed annual cycles. Source systems typically include student records system, research grant management, finance system, virtual learning environment and HR.

For a platform design, the consequence is specific. Statutory returns and internal management reporting are produced through separate routes, so published figures and internal figures disagree. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a student lifecycle model aligned to the statutory return definitions, which removes the annual manual assembly exercise.

Medical devices

Device history records, complaint and vigilance data, and post-market surveillance obligations that require linking field events back to manufacturing batches. Distributor-held data breaks traceability at exactly the point where a field action needs it.

Systems to plan for: ERP, QMS, complaint handling systems, PLM and distributor reporting. EU MDR post-market surveillance and vigilance reporting require a reproducible data trail from field event back to production batch.

Healthcare provision

Patient-level activity data of the highest sensitivity, with cohort and pathway definitions that must be stable and clinically defensible. Two reports on the same pathway include different patients, and clinical confidence collapses the first time the difference surfaces in a meeting. Health data attracts the stricter GDPR special-category conditions, and small-number suppression must be enforced in measures including drill-through.

A medallion layout for a higher education and research 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 Tübingen client in higher education and research.

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 higher education and research workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Tübingen 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 student records system.

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 Tübingen specifically, Germany West Central (Frankfurt) is the normal primary with Germany North (Berlin) as the pair. Latency from Tübingen 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 Tübingen 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: Heidelberg, Göttingen, Marburg, Leipzig, Bochum and Bielefeld.

Frequently asked questions