Microsoft Fabric and Azure data platforms in Edinburgh: 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 Edinburgh, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Edinburgh's base in asset and wealth management, retail and commercial banking, 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 one of Europe's larger asset management and pensions centres and a strong data science and informatics research base.

Does a Edinburgh 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 Edinburgh

Asset and wealth management

Position, transaction and valuation data arriving from custodians and portfolio systems on different cycles, with market data licensed under terms that restrict redistribution. Source systems typically include portfolio management system, custodian files, order management system, market data feeds and CRM.

For a platform design, the consequence is specific. Reporting built on the custodian feed and reporting built on the portfolio system disagree, and nobody owns the reconciliation between them. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a position and valuation model with an explicit as-at dimension, so every figure states the valuation date it was computed on.

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.

Software and technology

Product event streams at very high volume alongside subscription, billing and support data that must be joined at account grain to be useful. ARR comes from a finance spreadsheet with manual adjustments, so restating a prior period requires archaeology and diligence becomes painful. Product telemetry on identifiable users needs a lawful basis and consent handling, and SOC 2 or ISO 27001 evidence often covers the analytics estate.

A medallion layout for an asset and wealth management 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 Edinburgh client in asset and wealth management.

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 asset and wealth management workloads often are, while import still wins on smaller models with heavy DAX.

Migrating from what you have now

Few Edinburgh 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 portfolio management 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 Edinburgh specifically, UK South (London) is the normal primary with UK West (Cardiff) as the pair. Latency from Edinburgh 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 Edinburgh 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: London, Leeds, Bournemouth, Brighton and Hove, Cheltenham and Chester.

Frequently asked questions