Data pipelines and warehousing
The plumbing that moves data from the systems where it is created to the place where it can be analysed — running on schedule, correct when it lands, and loud when it isn't.
The pipeline nobody is watching
It runs nightly and has done for two years, so everyone assumes it is fine. In fact it started failing partially in March, and the dashboards have been subtly wrong since. Nothing raised an alarm, because nothing was checking — the job exits zero whether or not the data arrived.
You’ll recognise this if
- Reporting depends on somebody running an export by hand
- Analysts spend most of their time preparing data, not analysing it
- Numbers change retroactively and nobody can explain why
- A scheduled job exists that nobody understands well enough to change
What we actually deliver
Ingestion from your real sources
Databases, SaaS APIs, files, and event streams pulled in reliably, with incremental loading where volume justifies it and full refreshes where simplicity wins.
Transformations under version control
Business logic written as reviewable, tested code rather than accumulated in a scheduling tool's UI. A change can be proposed, reviewed, and reverted like any other.
Tests on the data itself
Checks that run every time: row counts within expected bounds, no unexpected nulls in key fields, totals reconciling to source. Failures stop the pipeline instead of publishing quietly wrong numbers.
A warehouse organised for the questions asked of it
Modelled so an analyst can find what they need without a tour from an engineer, with definitions documented next to the tables.
The way we approach it
One valuable dataset, end to end
We take the dataset that matters most through the whole path — ingestion, transformation, testing, dashboard — before broadening. It proves the architecture on something people care about.
Idempotent by default
Every job is safe to re-run. Recovery from a failure should be running it again, not an afternoon of manual repair.
Sized to your data, not to a conference talk
Most organisations' data fits comfortably in a well-configured warehouse. We recommend the simplest architecture that covers your volume, because someone has to operate it after we go.
What changes
- Reporting that refreshes on its own, without a manual step
- Failures that alert instead of publishing wrong numbers
- Analysts spending their time on analysis
- A warehouse your team can extend without calling us
Asked often enough to answer here
Which warehouse should we use?
For most mid-sized organisations, several would work fine, and the deciding factors are what your team knows and what your cloud commitments already are. We are far more concerned with modelling and testing than with the badge on the warehouse.
Do we need real-time data?
Rarely, and it is worth checking honestly. Streaming is markedly more complex to build and operate; most decisions are made on data that is a few hours old without anyone being worse off. Where it genuinely matters we will build it.
Can you fix pipelines we already have?
Yes, and it is often the better first step. Adding proper testing, alerting, and documentation to existing pipelines usually recovers more value per pound than replacing them.
Related
Wherever you’re starting from, let’s figure out the next step.
Tell us what you’re building — we’ll tell you honestly whether we’re the right team for it.