Microsoft Fabric and Azure data platforms in Carlow: 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 Carlow organisations find themselves in when they call us.

This page is about building a data platform for an organisation in Carlow, Microsoft Fabric, Azure Synapse, Data Factory and the lakehouse patterns underneath them, with the local economy taken seriously. Carlow's base in pharmaceuticals and biotech, agriculture and agribusiness, 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 biopharmaceutical manufacturing and agricultural machinery and services.

Does a Carlow 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 Carlow

Agriculture and agribusiness

Field, herd and yield data with weather dependence, subsidy and traceability reporting, and prices set by markets rather than by the business. Source systems typically include farm management systems, ERP, weather and satellite data, traceability platforms and subsidy reporting portals.

For a platform design, the consequence is specific. Subsidy and traceability reporting is produced separately from management reporting, so the same activity data is prepared twice differently. So the architecture has to make the raw grain replayable rather than aggregating at ingestion, and the first serving model should be a yield and margin model with time-versioned land and herd dimensions, plus traceability coverage reporting.

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.

Systems to plan for: CTMS and EDC, LIMS, MES, SAP ERP, pharmacovigilance systems and licensed market data. GxP data integrity expectations (attributable, legible, contemporaneous, original, accurate) apply to analytics pipelines handling regulated data.

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. Freedom of information and transparency duties mean published figures must be reproducible, and disclosure control has to be applied in the pipeline.

A medallion layout for an agriculture and agribusiness 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 Carlow client in agriculture and agribusiness.

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

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

Migrating from what you have now

Few Carlow 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 farm management systems.

Residency, latency and the Ireland question

North Europe sits in Dublin, so Irish organisations get in-country residency on the region most of Europe treats as its default. The practical consequence is excellent latency and a short list of residency questions, though cross-border processing still has to be documented where a service falls back to another region.

For Carlow specifically, North Europe (Dublin) is the normal choice. Latency from Carlow 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.

• Ireland hosts the EU establishment of many multinationals, so the DPC is frequently the lead supervisory authority, which raises the bar on analytics documentation well above local headcount.
• The Central Bank's outsourcing and operational resilience expectations apply to cloud analytics platforms used by regulated firms.
• EU-level obligations (DORA, NIS2, CSRD and the AI Act) apply in full, and the Irish implementation timetable is worth tracking per obligation.

Where to go next

If you are weighing a platform decision for a Carlow 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 Ireland and deliver into neighbouring markets regularly. Nearby pages worth reading if your group has sites elsewhere: Newbridge, Clonmel, Limerick, Galway, Waterford and Drogheda.

Frequently asked questions