Microsoft Fabric and Azure data platforms in Liège: 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 Liège, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Liège's base in logistics and freight, metals and materials, and aviation and airports 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 one of Europe's largest air cargo hubs and steel and metals processing heritage.

Does a Liège 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 Liège

Logistics and freight

Event data from scanners, telematics, EDI and transport management systems, each with its own timestamp semantics and time zone handling. Source systems typically include transport management system, warehouse management system, telematics, EDI messages and customs platforms.

For a platform design, the consequence is specific. Subcontracted volume arrives as spreadsheets, so a third of the network is effectively unmeasured exactly where control is weakest. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a canonical event model with normalised UTC timestamps and documented source precedence, which is the prerequisite for every duration measure.

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. Statutory returns and internal management reporting are produced through separate routes, so published figures and internal figures disagree.

Systems to plan for: student records system, research grant management, finance system, virtual learning environment and HR. Student data is personal data with additional duties around special-category attributes, and research data may carry funder-specific restrictions.

Aviation and airports

Operational data at flight and turnaround grain from several independent systems, with punctuality and baggage performance measured against externally defined standards. Handler, airline and airport each report different turnaround figures for the same aircraft, and nobody owns the reconciliation. Passenger data is personal data under GDPR with additional aviation security rules, so access and retention need explicit design.

A medallion layout for a logistics and freight 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 Liège client in logistics and freight.

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

Migrating from what you have now

Few Liège 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 transport management system.

Residency, latency and the Belgium question

Most Belgian deployments land in West Europe, which is physically in the Netherlands, or in France Central. Microsoft has announced a Belgian datacentre region, so check current availability before committing to an architecture that assumes in-country residency, and in the meantime document the EU-but-not-Belgian position for your data protection officer.

For Liège specifically, West Europe (Netherlands) is the normal primary with France Central (Paris) as the pair. Latency from Liège 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.

• Belgium's federal structure means some reporting obligations sit with the regions (Flanders, Wallonia and Brussels) rather than nationally, which multiplies the datasets a single organisation must produce.
• Language legislation affects employee-facing systems: reporting used in employment contexts may need to be available in the language of the region where staff are employed.
• As the host of the EU institutions, Brussels has an unusual concentration of organisations working to EU-level reporting rules ahead of national implementation.

Where to go next

If you are weighing a platform decision for a Liège 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 Belgium and deliver into neighbouring markets regularly. Nearby pages worth reading if your group has sites elsewhere: Charleroi, Kortrijk, La Louvière, Bruges, Mons and Hasselt.

Frequently asked questions