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Consistent dbt structure frees teams to decide on hard problems

How we structure our dbt projects

dbt Labs guide arguing that files, folders, naming, and patterns reduce decision fatigue in collaborative projects.

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How we structure our dbt projects | dbt Developer Hub

dbt Labs
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The overview argues that collaborative analytics work needs shared norms so limited decision capacity is not spent on where folders go or how files are named. A project's structure is its system of files, folders, naming conventions, and programming patterns for organizing transformations.

For data and content teams, an explicit, documented structure keeps the project approachable as more domains contribute. The guide presents itself as a starting point: teams may change the pattern, but should declare the reasoning and stay consistent.

Governed shared meaning depends on the same discipline: labels and groupings that move data from source-conformed inputs toward business-conformed outputs so many systems and people can read the same narrative of the company.

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Abstract

The guide says analytics engineering is collaboration at scale under limited decision bandwidth, so projects need consistent norms for folders, names, and patterns. Structure is how transformations are labeled, grouped, and combined. One shared principle is moving data from source-conformed shapes toward business-conformed ones, with consistency valued over any single style.

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dbtproject structurenaming conventionssource-conformedbusiness-conformedcollaborationData EngineeringContent OperationsOntology & TaxonomyData Governance
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