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

The question we are asked most often by Leverkusen 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 Leverkusen, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Leverkusen's base in chemicals and process industry, pharmaceuticals and biotech, 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 a major integrated chemical and pharmaceutical production site and process industry services.

Does a Leverkusen 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 Leverkusen

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. Source systems typically include process historian, batch management system, LIMS, SAP ERP and environmental monitoring.

For a platform design, the consequence is specific. Historian data is retained for months while investigations and environmental questions reach back years. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a batch genealogy and yield model with energy and utilities allocation, which serves production, cost accounting and environmental reporting at once.

Pharmaceuticals and biotech

Regulated data across research, clinical, manufacturing and commercial domains, with validation requirements that make casual changes to a pipeline expensive. Commercial and safety data share a workspace, which creates a compliance exposure that is painful to unwind once reports exist.

Systems to plan for: CTMS and EDC, LIMS, MES, SAP ERP, pharmacovigilance systems and licensed market data. GxP data integrity expectations (attributable, legible, contemporaneous, original, accurate) apply to analytics pipelines handling regulated data.

Logistics and freight

Event data from scanners, telematics, EDI and transport management systems, each with its own timestamp semantics and time zone handling. Subcontracted volume arrives as spreadsheets, so a third of the network is effectively unmeasured exactly where control is weakest. Drivers' hours records, customs declarations and posting documentation all carry retention obligations and are frequently requested in audit.

A medallion layout for a chemicals and process industry 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 Leverkusen client in chemicals and process industry.

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

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 chemicals and process industry workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Leverkusen 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 process historian.

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 Leverkusen specifically, Germany West Central (Frankfurt) is the normal primary with Germany North (Berlin) as the pair. Latency from Leverkusen 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 Leverkusen 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: Ludwigshafen am Rhein, Mannheim, Nuremberg, Duisburg, Wuppertal and Mönchengladbach.

Frequently asked questions