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

The question we are asked most often by Glasgow organisations is whether they need Microsoft Fabric. The answer is frequently no, and saying so has cost us work we did not want.

This page is about building a data platform for an organisation in Glasgow, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Glasgow's base in retail and commercial banking, insurance and reinsurance, and industrial machinery and engineering 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 Scotland's largest financial services and shared-services base and engineering and shipbuilding activity on the Clyde.

Does a Glasgow organisation actually need Fabric?

The case for Fabric gets strong when more than one of these holds: import models under strain, the same data needed through both SQL and notebooks, governed self-service for several teams, retention that a desktop model cannot carry, or a Premium capacity already on the bill. 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 Glasgow

Retail and commercial banking

High-volume transactional data at account and posting grain, with a core banking system that was never designed to be queried analytically and a general ledger that is authoritative but coarse. Source systems typically include core banking platform, general ledger, card and payment processors, credit decisioning systems and CRM.

For a platform design, the consequence is specific. Each function builds its own extract from core banking with its own as-at timing, and the resulting small variances are never reconciled. They are simply explained away each month. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a conformed account and customer dimension with a balance and transaction fact, which immediately resolves the usual disagreement between finance, risk and the front line.

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.

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. Claims data frequently includes health and criminal-conviction information, which attracts the stricter GDPR special-category conditions.

A medallion layout for a retail and commercial banking 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 Glasgow client in retail and commercial banking.

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 removes both the import refresh and the DirectQuery hop by reading Delta in place. It also carries a condition most teams discover late: unsupported constructs silently fall back, and the model then performs like DirectQuery. 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 retail and commercial banking workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Glasgow 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 core banking platform.

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 Glasgow specifically, UK South (London) is the normal primary with UK West (Cardiff) as the pair. Latency from Glasgow 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 Glasgow 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: Birmingham, Cardiff, Northampton, Norwich, Peterborough and Portsmouth.

Frequently asked questions