dbt-labs/dbt has 14,133 GitHub stars and 2,665 forks. GitHot measured +2 stars in 24 hours and +25 in 7 days. Last checked 2026-11-08 12:43 UTC. Last push 2026-11-04.
dbt를 사용하면 데이터 분석가와 데이터 엔지니어가 소프트웨어 엔지니어가 애플리케이션을 개발할 때 사용하는 것과 같은 방식으로 데이터를 변환할 수 있습니다.
측정된 스타 증가
- in 24h
- +2
- in 7 days
- +25
- in 15 days
- 업데이트 지연
- in 1 month
- 업데이트 지연
서로 겹치는 기간의 순증가량이며 일별 합계가 아닙니다. 측정값이 없으면 비워 둡니다. 스타는 관심도를 나타내며 품질을 보장하지 않습니다.
README.md
Original
[!WARNING] dbt v1 development has moved to the
1.latestbranch. Themainbranch 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 to1.latest.
dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

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.

Getting started
- Install dbt
- Read the introduction and viewpoint
- Explore the dbt platform for an enhanced collaboration experience.
Join the dbt Community
- Be part of the conversation in the dbt Community Slack
- Read more on the dbt Community Discourse
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.
관련 프로젝트 살펴보기
Asabeneh/30-Days-Of-Python
30 Days of Python 프로그래밍 챌린지는 Python 프로그래밍 언어를 30일 동안 배우기 위한 단계별 가이드입니다. 이 챌린지는 100일 이상 걸릴 수 있습니다. 자신의 속도에 맞춰 진행하세요. 다음 동영상도 도움이 될 수 있습니다: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
78,423 스타grafana/grafana
개방적이고 조합 가능한 옵저버빌리티 및 데이터 시각화 플랫폼입니다. Prometheus, Loki, Elasticsearch, InfluxDB, Postgres 등 여러 소스의 메트릭, 로그, 트레이스를 시각화합니다.
77,763 스타redis/redis
실시간 데이터 기반 애플리케이션을 구축하는 개발자에게 Redis는 가장 선호되고, 가장 빠르며, 기능이 가장 풍부한 캐시, 데이터 구조 서버이자 문서 및 벡터 쿼리 엔진입니다.
77,309 스타apache/superset
Apache Superset는 데이터 시각화 및 데이터 탐색 플랫폼입니다.
75,453 스타