Learning Data

Topics › Analytics engineering › In the weeds of data crafting

Written once: one home per fact, and everything else generated

A definition copied into four places drifts in three. Give each fact one home: meaning in the conceptual model, taken from the business glossary where it already defines the term, decisions with why and who in a log beside what they're about, accepted gaps on the model as known limitations, what the build uses in dbt's YAML. Generate the rest, sync it one way, and ship a breaking change as a new version with a date. The next film follows one question from start to end.

5½ min4 labs8 scenariosPause and thinkEnglish captions

Builds on An agent on the team

Watch the film

Press Play, or pick a chapter. Turn on Pause and think to stop for one question at the end of chapters.

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See it in the project

Every card in the film shows a real file from the example dbt project. Open the ones each chapter shows, on GitHub, or run the whole project yourself.

Think it through

The four Pause and think questions, for a class or a team. Open one to see the answer.

In the series: In the weeds of data crafting

Film 9 of 10.

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An agent on the team

An AI agent drafts in minutes. It follows rules written in the project, shows the query behind every claim, never weakens a test, and people approve.

5½ min · 4 labs · 8 scenarios · In the weeds of data crafting

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End to end

One question, ten steps, ten commits. Every file in its place, for as long as it's needed.

8½ min · 4 labs · 8 scenarios · In the weeds of data crafting

  1. 1Declare it, then build it
  2. 2Start from a question
  3. 3What makes it the same one
  4. 4One row of what, and when
  5. 5Promises and proofs
  6. 6Built in layers
  7. 7Who owns what
  8. 8An agent on the team
  9. 9Written once
  10. 10End to end