Skills Data Science Data Transformation Patterns Using dbt

Data Transformation Patterns Using dbt

v20260423
dbt-transformation-patterns
A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.
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Overview

dbt Transformation Patterns

Production-ready patterns for dbt (data build tool) including model organization, testing strategies, documentation, and incremental processing.

Use this skill when

  • Building data transformation pipelines with dbt
  • Organizing models into staging, intermediate, and marts layers
  • Implementing data quality tests and documentation
  • Creating incremental models for large datasets
  • Setting up dbt project structure and conventions

Do not use this skill when

  • The project is not using dbt or a warehouse-backed workflow
  • You only need ad-hoc SQL queries
  • There is no access to source data or schemas

Instructions

  • Define model layers, naming, and ownership.
  • Implement tests, documentation, and freshness checks.
  • Choose materializations and incremental strategies.
  • Optimize runs with selectors and CI workflows.
  • If detailed patterns are required, open resources/implementation-playbook.md.

Resources

  • resources/implementation-playbook.md for detailed dbt patterns and examples.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Info
Category Data Science
Name dbt-transformation-patterns
Version v20260423
Size 4.63KB
Updated At 2026-04-24
Language