dbt-oss 2.0.3

Note: v2 of the dbt engine is now available, and ships as two distributions:

• pip install dbt (recommended) - installs the default distribution with the full feature set
• pip install dbt-oss - installs the subset distribution, with only Apache 2 open source code

With v2, you no longer need to install adapters separately, so you can remove pip install dbt-<adapter> from your installation. See the install docs to get started.

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 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:

• 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

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.