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

Before deciding on a lakehouse, it is worth establishing whether your data actually outgrew an import model or simply outgrew the person maintaining it.

This page is about building a data platform for an organisation in Chemnitz, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Chemnitz's base in industrial machinery and engineering, automotive and mobility, and electronics and semiconductors 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 machine tool and automotive component manufacturing and a technical university with production engineering strength.

Does a Chemnitz 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 Chemnitz

Automotive and mobility

Deep traceability data spanning part, batch, vehicle and plant, with customer release schedules that change weekly and warranty data arriving months after production. Source systems typically include SAP ERP, MES, quality management system, EDI customer schedules, warranty claims systems and PLM.

For a platform design, the consequence is specific. Customer scorecards and internal quality reporting use different denominators, so the plant believes it is performing while the customer escalates. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a traceability model linking batch and part to vehicle and claim, which is also what makes a recall survivable.

Electronics and semiconductors

Very high volume test and yield data per die or unit, complex multi-stage processes, and component supply with long and volatile lead times. Test data is aggregated at ingestion, so when a field failure pattern emerges the unit-level evidence no longer exists.

Systems to plan for: MES, test and yield analysis systems, ERP, PLM and supplier portals. Export control classification applies to many components and to the associated technical 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 an automotive and mobility 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 Chemnitz client in automotive and mobility.

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

Direct Lake reads Delta files in OneLake with no import refresh and no DirectQuery round trip, but unsupported constructs trigger fallback and performance then depends on the SQL endpoint. 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 automotive and mobility workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Chemnitz 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 SAP ERP.

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 Chemnitz specifically, Germany West Central (Frankfurt) is the normal primary with Germany North (Berlin) as the pair. Latency from Chemnitz 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 Chemnitz 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: Regensburg, Hildesheim, Braunschweig, Aachen, Osnabrück and Paderborn.

Frequently asked questions