An analytics engineer writing transformation code across two dark-mode monitors

Platform

dbt

A way of writing the logic that turns raw data into reportable tables, with proper version control, testing and documentation built in.

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What works fine at thirty models becomes a real problem at three hundred.

What we do with dbt

Setting it up properly

Development and production environments, automated testing on every change, and a release process that does not depend on one person knowing the trick.

Organising it by business area

Once there are more than about a hundred models, a single flat folder stops being navigable. People give up searching and build a duplicate instead, which is how you end up with three versions of revenue.

Making the slow parts fast

Rebuilding everything every night gets expensive. We change the heaviest models to process only what is new, and write down how to correct them when late data arrives.

Tests that people act on

Automatic checks on every table, stricter ones on anything you report externally, and severity levels so a red result actually means someone should do something.

Knowing what a change will affect

Each business area has a named reviewer, and before a change goes through you can see which reports and which teams it touches.

Works with

GitHub, GitLab or Bitbucket

Your transformation logic lives in the same place as the rest of your code, reviewed the same way.

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

Three colleagues reviewing work on a monitor together

The people who pitch do the work

Small senior teams. There is no arrangement here where an experienced partner wins the engagement and somebody else delivers it.

The rest of the stack we work in

Common questions

dbt Core or dbt Cloud?

Either works. Core plus your existing tooling is often cheaper and gives more control if you already have strong engineers. Cloud earns its cost where you do not.

Our builds take an hour. Where do we start?

Usually with the ten slowest models. The time is almost never spread evenly, so a small number of changes tends to fix most of it.

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.