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Platform

Microsoft Azure

Cloud infrastructure to run your data platform on, from storage and processing through to the analytics services that sit on top of it.

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Azure gives you every building block. Working out which ones you actually need is the harder part.

The Microsoft Azure portal showing resources and services

What we do with Microsoft Azure

Storage and databases

Data Lake Storage, SQL Database and Cosmos DB, chosen for what you are genuinely storing rather than picked off a menu, and structured so the cost stays predictable as it grows.

Fabric and Synapse

The analytics layer, organised by business area so people can find things, with capacity sized against how you will actually use it rather than the number on the price list.

Data Factory

Bringing data in from your source systems on a schedule, with checks at the entry point so a change someone makes elsewhere stops the process rather than quietly corrupting your reports.

Azure AI and machine learning

Where a model genuinely earns its place. We will tell you when something simpler would do the same job with far less to maintain.

Identity, security and compliance

Access managed through Entra ID, with a record of who could see what and when, which is the question an auditor actually asks.

Hybrid setups

Where some systems have to stay where they are, connected properly rather than worked around with a nightly export.

Works with

Compute

Virtual machines, containers and serverless functions, so you pay for the shape of work you actually run.

Global infrastructure

Regions worldwide with high availability and low latency, which matters if your users are not all in one place.

Microsoft 365, Dynamics and Power BI

If you already run these, the identity and reporting joins are the part you do not have to build.

What clients use it for

Hosting and scaling web applications

Running the applications themselves, not just the data behind them.

Hybrid setups

Where some systems have to stay on your own hardware and still need to work with everything else.

AI and machine learning

Prebuilt models and custom ones, where a model genuinely earns its keep.

Where it fits

We are not a reseller and we take no vendor commissions, so there is nothing riding on which platform you choose. What we care about is whether the setup can be operated by your team after we leave.

  • Recommended where it genuinely solves a problem you have, not as a default
  • Implemented as code in your repository, under your accounts
  • Handed over with runbooks for the failure modes we actually hit
The Azure OpenAI studio, where models are deployed and tested against your own data
Two engineers working at a rack of servers

We build it so your team can run it

Everything sits in your accounts, in your repository, from day one. No lock-in and nothing that depends on us remembering how it works.

The rest of the stack we work in

Common questions

Is Fabric ready for real use?

For most of what we see, yes. The thing that catches people out is that paying for capacity works quite differently from Synapse, and the bill can surprise you. We model that before recommending a move.

We already use Microsoft 365. Does that make Azure the obvious choice?

It helps, and it is not decisive on its own. The way it fits with Power BI and Entra ID is genuinely convenient. We would still check the numbers against the alternatives rather than assume.

Should we move off Synapse?

Not automatically. If what you have is stable and the cost is understood, moving is a project with real risk attached. We would want a specific reason, not just newness.

Other platforms we work in

dbt

Snowflake

Databricks

Ready to turn complexity into your next advantage?

Tell us what you are running today and what is not working. That is a more useful starting point than a platform comparison.

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