Microsoft Fabric and Azure data platforms in Duisburg: 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 Duisburg, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Duisburg's base in ports, terminals and maritime, metals and materials, and logistics and freight 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 world's largest inland port and steel production and processing.

Does a Duisburg 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 Duisburg

Metals and materials

Heat and coil genealogy, energy-intensive process data, and quality certification per batch, with energy cost a dominant and volatile input. Source systems typically include process control systems, ERP, quality certification systems, energy monitoring and logistics systems.

For a platform design, the consequence is specific. Energy and production data are reported on different period bases, so the cost per tonne figure moves for reasons nobody can explain. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a genealogy and yield model with energy allocation, which supports both operational improvement and emissions reporting from one source.

Chemicals and process industry

Continuous process data at high frequency, batch genealogy, yield and energy intensity, with safety and environmental reporting drawing on the same sources. Historian data is retained for months while investigations and environmental questions reach back years.

Systems to plan for: process historian, batch management system, LIMS, SAP ERP and environmental monitoring. REACH, CLP and environmental permit reporting all require auditable activity data with a documented calculation method.

Ports, terminals and maritime

Vessel, container and yard movement data at very high event volume, with berth planning and customs status driving operational decisions hour by hour. Equipment telemetry and the terminal operating system disagree on what counts as a move, so productivity figures vary by source. Customs and security data is tightly controlled, and NIS2 applies to many port operators as critical infrastructure.

A medallion layout for a metals and materials 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 Duisburg client in metals and materials.

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

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 metals and materials workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Duisburg 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 process control 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 Duisburg specifically, Germany West Central (Frankfurt) is the normal primary with Germany North (Berlin) as the pair. Latency from Duisburg 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 Duisburg 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: Halle (Saale), Krefeld, Ludwigshafen am Rhein, Leverkusen, Cologne and Hamburg.

Frequently asked questions