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dbt

12 Aug 2023

dbt (short for “data build tool”) is an open-source software tool that enables data analysts and engineers to transform and model data in the data warehouse. It’s particularly popular among modern data stacks where ELT (Extract, Load, Transform) patterns have become prevalent over traditional ETL patterns.

Here’s a brief overview of dbt:

  1. SQL-Based Transformations: With dbt, transformations are written in SQL, a language familiar to many data professionals. This means analysts can write, document, and maintain data transformation workflows without relying on more specialized languages or tools.
  2. Version Control: dbt projects are inherently structured for git version control. This allows for collaborative work, change tracking, and version history of your data models.
  3. Testing: dbt has built-in capabilities for testing data models. Users can write tests to ensure that data meets specific criteria (e.g., uniqueness, non-null values) and ensure data integrity.
  4. Documentation: dbt can auto-generate documentation for your data models. This makes it easier for teams to understand the data transformations, lineage, and logic applied.
  5. Modularity and Reusability: dbt promotes using modular SQL through models, macros, and packages. This allows for code reusability and standardized patterns across large projects.
  6. CLI and Integration: dbt has a robust command-line interface and integrates well with other tools in the modern data stack. This makes it a flexible and powerful part of CI/CD workflows for data transformation and modeling.
  7. Extensibility: Through its package manager (dbt Hub), users can find and share dbt packages, which are sets of dbt models, macros, and more. This allows for the community-driven expansion of dbt’s capabilities.
  8. Platform Compatibility: dbt works with many modern data warehousings solutions like Snowflake, BigQuery, Redshift, and more.

What makes dbt unique?

  • Focus on Analysts: dbt is built for analysts familiar with SQL, empowering them to own more of the data transformation and modeling process without relying on specialized ETL tools or engineering resources.
  • Community and Collaboration: dbt has a thriving community of users and contributors. The community shares best practices, packages and supports one another, driving rapid innovation and evolution of the tool.
  • Shift towards ELT: dbt fits nicely into the ELT paradigm, where raw data is first loaded into a modern data warehouse and then transformed. This differs from traditional ETL processes where transformation occurs before loading.
  • Transparency and Code-as-Documentation: Because dbt models are code (SQL), they serve as self-documenting artifacts that outline precisely how data is transformed, providing transparency into the data transformation process.
  • Integrated Testing and Documentation: The ability to embed tests and generate documentation directly within dbt streamlines processes that were traditionally more disjointed in many data workflows.

Dbt offers a unique blend of capabilities tailored for modern data teams, emphasizing transparency, collaboration, and empowering analysts.

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