ETL & Data Pipelines

Scheduled, logged, monitored — instead of a person and a spreadsheet.

The pipeline that needs building is rarely the interesting part. The part that matters is what happens when a source is late, a format changes, or a row does not match — and whether anyone learns about it before a report is wrong.

What this covers

See it in detail

The integration showcase covers the patterns with real code. The label service is one example of the shape: a program per label became one configuration-driven service.

Who this is for

Operations where a real process depends on somebody running something. The workstation macro, the export that gets reshaped in Excel, the folder a file has to land in by seven. It works, and it works until the person is away or the machine is replaced.

The tell that this is the right service rather than integration: the data does not need to move in both directions, it needs to move reliably in one.

What we would look at first

What determines the cost

How many sources, and whether their formats are owned by you or by somebody else. Whether the existing logic is written down anywhere or only in one person's habit — recovering an undocumented process is its own piece of work and is scoped separately rather than absorbed quietly. Whether the destination needs history or only the current state. And whether anyone has ever specified what a correct run looks like, because that specification is what makes the monitoring possible at all.

Talk about a pipeline