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

A Fabric capacity bought before anyone has profiled the workloads is an expense in search of a benefit. That is the position several Exeter organisations find themselves in when they call us.

This page is about building a data platform for an organisation in Exeter, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Exeter's base in public sector, higher education and research, and insurance and reinsurance 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 national meteorological and environmental data activity and a research university with strong environmental science.

Does a Exeter 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 Exeter

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. Source systems typically include student records system, research grant management, finance system, virtual learning environment and HR.

For a platform design, the consequence is specific. Statutory returns and internal management reporting are produced through separate routes, so published figures and internal figures disagree. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a student lifecycle model aligned to the statutory return definitions, which removes the annual manual assembly exercise.

Insurance and reinsurance

Policy, premium and claims data where the same risk is represented differently in underwriting, finance and claims systems, and where development over time is the whole point. Actuarial results land as spreadsheets with no lineage, so the reported reserve movement cannot be tied back to the model run that produced it.

Systems to plan for: policy administration, claims management, actuarial reserving models, reinsurance systems and broker feeds. Claims data frequently includes health and criminal-conviction information, which attracts the stricter GDPR special-category conditions.

Tourism, hospitality and events

Booking, channel and rate data with heavy seasonality, plus on-site spend and occupancy that only reconcile once channel commission is accounted for. Gross booking value from online travel agents is compared with direct booking revenue, making the most expensive channel look like the best one. Guest data is personal data with cross-border transfer implications when group systems sit outside the region.

A medallion layout for a higher education and research 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 Exeter client in higher education and research.

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.

We size capacity by measuring rather than estimating: each workload profiled for compute shape, background refresh against interactive query, concurrency and peak hours, then validated on a short-lived trial capacity with your real 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

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 higher education and research workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Exeter 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 student records system.

Residency, latency and the United Kingdom question

Azure UK South and UK West give genuine in-country data residency, and Power BI tenants can be provisioned in the United Kingdom region. Note that some workloads, certain preview features and a handful of AI services, still process outside the region, so residency claims should be checked per service rather than assumed tenant-wide.

For Exeter specifically, UK South (London) is the normal primary with UK West (Cardiff) as the pair. Latency from Exeter 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.

• UK GDPR data-minimisation and storage-limitation duties apply directly to analytics copies of personal data, which is where most estates quietly fail.
• Consumer Duty outcome monitoring has made customer-level analytics a supervisory expectation for regulated firms, not an optional capability.
• SECR energy and carbon reporting requires auditable activity data, which usually means the same lineage discipline as financial reporting.

Where to go next

If you are weighing a platform decision for a Exeter 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 United Kingdom and deliver into neighbouring markets regularly. Nearby pages worth reading if your group has sites elsewhere: Stirling, Norwich, York, Winchester, Perth and Glasgow.

Frequently asked questions