dbt README

The project's README from github.com/dbt-labs/dbt. As one markdown file: README.md.

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[!WARNING] dbt v1 development has moved to the 1.latest branch. The main branch now contains all the Apache 2.0 source code of dbt v2.0 — a ground-up rewrite of dbt in Rust. If you're looking for the v1 Python implementation of the dbt framework, switch to 1.latest.

dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

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About dbt v2.0

🚧 dbt v2.0 is in beta. Behavior, APIs, and on-disk formats may change before the stable release.

dbt v2.0 is engineered for performance at scale. It parses, compiles, and runs projects in a fraction of the time compared to v1. The source code in this repository is available to everyone under the standard Apache 2.0 license. dbt is a distribution of the dbt repository with dbt-specific customizations released under a dbt product license.

The big shifts from v1:

Supported operating systems and architectures

dbt v2.0 and its drivers are compiled per operating system and architecture.

Legend:

Operating system x86-64 ARM
macOS 🟢 🟢
Linux 🟢 🟢
Windows 🟢 🟡

Understanding dbt

Analysts using dbt can transform their data by simply writing select statements, while dbt handles turning these statements into tables and views in a data warehouse.

These select statements, or "models", form a dbt project. Models frequently build on top of one another – dbt makes it easy to manage relationships between models, and visualize these relationships, as well as assure the quality of your transformations through testing.

dbt dag

Getting started

Join the dbt Community

Reporting bugs and contributing code

Code of Conduct

Everyone interacting in the dbt project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the dbt Code of Conduct.

License

The source code in this repository is licensed under the Apache License 2.0.

dbt (dbt-labs/dbt)

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What is this?dbt lets data analysts and engineers transform data in their warehouse by writing SQL select statements ('models'), applying the same practices — version control, testing, modularity — that software engineers use to build applications.
Is this a Python tool or something else now?dbt v2.0 is a ground-up rewrite of dbt in Rust, distributed as a single self-contained CLI binary instead of a Python package; the older v1 Python implementation still exists on a separate branch.
How do I get started with dbt?Install the dbt v2.0 binary for your OS/architecture, then write SQL select statements as 'models' inside a dbt project; dbt turns them into tables and views in your warehouse.
Do I need Python to run dbt v2.0?No — unlike v1, v2.0 ships as a single self-contained binary and needs no Python runtime or dependency management.
Is dbt v2.0 ready for production use?It's still in beta, so behavior, APIs, and on-disk formats may change before the stable release; Windows support is also limited to x86-64 (ARM isn't supported yet).
Which operating systems and chips does it support?macOS and Linux are supported on both x86-64 and ARM; Windows is supported on x86-64 only, with ARM support marked as not yet available.
What happened to the old Python version of dbt?dbt v1 development moved to the 1.latest branch; the main branch now holds the new Rust-based v2.0 rewrite.
What exactly is a 'model' in dbt?A model is just a SQL select statement that dbt compiles and materializes into a table or view in your data warehouse, with dbt managing dependencies between models.
Why did the dbt team rewrite it in Rust?For performance at scale — v2.0 parses, compiles, and runs projects in a fraction of the time compared to the v1 Python implementation, especially on large projects.
What's new about the artifacts dbt produces?v2.0 generates Parquet artifacts that can be easily queried and joined to analyze a project, while still producing the original JSON artifacts like manifest.json for backward compatibility.
What are similar projects?walshadow, airbyte, hotdata-cli, cube, ClickHouse, pgterm, superset, trino
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