Microsoft Fabric and Azure data platforms in Charleroi: 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 Charleroi, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Charleroi's base in aviation and airports, industrial machinery and engineering, and pharmaceuticals and biotech 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 low-cost airline airport and a biotechnology and pharmaceutical cluster at Gosselies.

Does a Charleroi 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 Charleroi

Aviation and airports

Operational data at flight and turnaround grain from several independent systems, with punctuality and baggage performance measured against externally defined standards. Source systems typically include flight operations systems, airport operational database, baggage handling systems, crew management and revenue accounting.

For a platform design, the consequence is specific. Handler, airline and airport each report different turnaround figures for the same aircraft, and nobody owns the reconciliation. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a flight and turnaround model with reconciled milestone definitions, which is what makes punctuality root-cause analysis possible.

Public sector

Case and service data locked inside line-of-business systems with restrictive reporting modules, feeding statutory returns with precisely defined rules. Returns are assembled by hand each cycle by a small number of experienced staff, concentrating risk in individuals.

Systems to plan for: case management systems, finance and ERP, HR and payroll, GIS and gazetteers and grant management. Freedom of information and transparency duties mean published figures must be reproducible, and disclosure control has to be applied in the pipeline.

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. GxP data integrity expectations (attributable, legible, contemporaneous, original, accurate) apply to analytics pipelines handling regulated data.

A medallion layout for an aviation and airports 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 Charleroi client in aviation and airports.

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

Migrating from what you have now

Few Charleroi 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 keep the old platform running until each domain is proven. The alternative, moving everything at once, is how migrations end up reversed. The reference pipeline we build first is usually the system that hurts most, which here is normally flight operations systems.

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 Charleroi specifically, West Europe (Netherlands) is the normal primary with France Central (Paris) as the pair. Latency from Charleroi 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 Charleroi 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: Liège, Hasselt, La Louvière, Genk, Verviers and Turnhout.

Frequently asked questions