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dbt-labs/dbt has 14,136 GitHub stars and 2,665 forks. GitHot measured +24 in 7 days. Last checked 2026-11-09 11:43 UTC. Last push 2026-11-04.

这些数字如何统计

dbt 帮助数据分析师和数据工程师运用软件工程师构建应用程序时采用的实践来转换数据。

实测星标增长

24小时
更新延迟
7天
+24
15天
更新延迟
1个月
更新延迟

各周期内的星标净变化,周期相互重叠,并非每日新增。缺失的测量保持空白。星标代表关注度,不代表软件质量。

README.md

原文

dbt logo

dbt CI status

[!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.

architecture

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 is a distribution of the dbt repository with dbt-specific customizations released under a dbt product license.

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 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

  • Want to report a bug or request a feature? Let us know and open an issue
  • Want to help us build dbt? Check out the Contributing Guide

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.

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