Microsoft Fabric and Azure data platforms in Essen: 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 Essen, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Essen's base in energy generation and supply, networks and water utilities, and professional and legal services 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 headquarters functions for major energy and utility groups and engineering and construction services.

Does a Essen organisation actually need Fabric?

Look for a cluster rather than a single trigger: import refreshes that are getting uncomfortable, demand for SQL and notebooks against one copy, self-service across more than one team, retention that rules out a desktop model, or an existing Premium commitment. 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 Essen

Networks and water utilities

Asset registers that disagree with the physical network, interruption and leakage data feeding regulatory submissions, and long-lived assets needing time-versioned records. Source systems typically include GIS and network model, asset management system, SCADA, works management and customer systems.

For a platform design, the consequence is specific. Three systems hold three versions of what is installed where, so condition reporting cannot be trusted at the asset level where decisions are made. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a reconciled asset model across GIS, asset management and works systems, which is the precondition for credible investment reporting.

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.

Systems to plan for: practice management system, time recording, professional services automation, CRM and finance system. Matter confidentiality and information barriers must be inherited by analytics through row-level security, not re-implemented loosely.

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 networks and water utilities 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 Essen client in networks and water utilities.

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.

Sizing comes from a trial capacity running your real workloads, profiled for compute shape, refresh against query load, concurrency and peak hours. Estimating it from user counts is how organisations end up throttled at month end. 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

With Direct Lake the model reads Delta directly from OneLake, so there is no refresh to schedule and no query round trip. The catch is the condition attached: anything unsupported falls back, and you are then on SQL endpoint performance without having chosen it. 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 networks and water utilities workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Essen 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. What both have in common is that nothing is switched over in a single weekend. Single-weekend cutovers are what produce the rollbacks. The reference pipeline we build first is usually the system that hurts most, which here is normally GIS and network model.

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 Essen specifically, Germany West Central (Frankfurt) is the normal primary with Germany North (Berlin) as the pair. Latency from Essen 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 Essen 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, Stuttgart, Frankfurt am Main and Düsseldorf.

Frequently asked questions