dbt

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  <img width="750" alt="dbt logo" src="assets/dbt-logo-full.png" />
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  <a href="https://github.com/dbt-labs/dbt/actions/workflows/ci.yml?query=branch%3Amain"><img src="https://github.com/dbt-labs/dbt/actions/workflows/ci.yml/badge.svg?branch=main&amp;event=push" alt="dbt CI status"/></a>
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> [!WARNING]
> **dbt v1 development has moved to the [`1.latest`](https://github.com/dbt-labs/dbt/tree/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`](https://github.com/dbt-labs/dbt/tree/1.latest).

**[dbt](https://www.getdbt.com/)** enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

![architecture](https://raw.githubusercontent.com/dbt-labs/dbt/202cb7e51e218c7b29eb3b11ad058bd56b7739de/etc/dbt-transform.png)

## About dbt v2.0

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](https://docs.getdbt.com/docs/introduction) is a distribution of the dbt repository with dbt-specific customizations released under a [dbt product license](https://www.getdbt.com/dbt-fusion-engine-license-agreement).

The big shifts from v1:

- **Faster** — parse and compile times are dramatically improved, especially on the largest dbt projects.
- **Stricter** — a tightly-defined language specification enforces correctness at parse time.
- **More scalable artifacts** — v2.0 produces Parquet artifacts that can be easily queried, joined, and analyzed to understand your dbt project. The artifacts encompass everything in the JSON artifacts (e.g. `manifest.json`), which continue to be produced for backwards compatibility.
- **Easier to install** — distributed as a single self-contained binary, with no Python runtime or dependency management required.
- **A completely revamped local documentation experience** — dbt docs is now powered by those new artifacts and capable of scaling to large projects.

### Supported operating systems and architectures

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

Legend:
* 🟢 — Supported today
* 🟡 — Not yet supported

| 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](https://docs.getdbt.com/docs/ref) between models, and [visualize these relationships](h