dbt Analytics Engineering Tool

dbt-core 1.12.0 - Alecia Beth Moore-Hart (P!NK) (July 16, 2026)

Breaking Changes

• Remove the experimental dbt login command and the bundled dbt-state plugin (including --manage-state/manage_state and the DBT_ENGINE_STATE_* env vars), reverting PRs (#NA)
• Flags introduced in 1.9 and 1.10 defaults to true (#12713)

Features

• Make MAXIMUM_SEED_SIZE_MIB configurable (#7117, #7124)
• Support partial parsing for function nodes (#12072)
• Add UnparsedMetricV2 to read in new-style YAML Semantic Layer Metrics. (#12157)
• Allow for defining funciton arguments with default values (#12044)
• Add directory change instruction after dbt init (#9041)
• Raise jsonschema-based deprecation warnings by default (#12240)
• :bug: :snowman: Disable unit tests whose model is disabled (#10540)
• Implement config.meta_get and config.meta_require (#12012)
• Deprecate null return values from generate_schema_name macro, behind require_valid_schema_from_generate_schema_name flag (#12347)
• Add ability to indicate dbt Model also represents a Semantic Model. (Not fully supported yet.) (#.)
• Added ability to parse semantic layer dimensions added to columns as part of new YAML. (Not ready for use until more of the new yaml is implemented.) (#.)
• Add parsing for new YAML for semantic layer entities attached to dbt models. (#12395)
• Process semantic metrics in v2 YAML if they are not merged into a model. (#12161)
• Add parsing for v2 metrics on models and finish implementing their dependency behaviors. (#12397)
• Add the ability to process derived semantic entities to dbt models, as required by new YAML schema. (#12401)
• Enable parsing derived dimensions for v2 semantic layer YAML. (#12404)
• Add unit tests to the Jinja graph object, enabling tools like dbt-project-evaluator to run checks on unit tests. (#12033)
• Implement agg_time_dimension for new semantic YAML. (#12410)
• Implement parsing of semantic model object-style configuration in v2 semantic YAML. (#12413)
• Implement primary_entity field for semantic models in semantic YAML v2. (#12414)
• add config.meta_get to python model parsing (#12458)
• Allow jinja suffixed extensions for markdown and sql files (#11097, #3484)
• Allow continue running child on parent error (#2142)
• Added support for vars.yml to declare project variables (#11144, #2955)
• execute dbt debug logic after creating a new project in dbt init ([#12510](https://github.com/dbt-labs/

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<img src="https://raw.githubusercontent.com/dbt-labs/dbt-core/fa1ea14ddfb1d5ae319d5141844910dd53ab2834/etc/dbt-core.svg" alt="dbt logo" width="750"/>

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[!WARNING]

dbt Core v1 development has moved to the 1.latest branch.

The main branch now hosts dbt Core v2.0 (alpha) — a ground-up rewrite in Rust that is the foundation of the Fusion engine. If you're looking for the Python implementation of dbt Core, 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 Core v2.0

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

dbt Core v2.0 is engineered for performance at scale — parsing, compiling, and running projects in a fraction of the time compared to v1. It's released under the Apache 2.0 license and is the foundation of the Fusion engine.

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 Core 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

Start by choosing a distribution. dbt Core is the baseline distribution of dbt. Fusion extends dbt Core with additional SQL comprehension abilities. Both distributions are free to install and can run locally.

• If you need an Apache 2.0 licensed tool and the ability to review every line of code inside of it, install dbt Core.
• If you need a free CLI you can use locally, install Fusion. It can do more than dbt Core out of the box and you can seamlessly enable other advanced features over time if you choose to.

Regardless of the distribution you choose, each is part of a single framework with a single language specification, meaning your business logic is portable in both directions.

Explore the dbt platform for an enhanced collaboration experience.

Read the introduction and viewpoint

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

dbt Core is licensed under the Apache License 2.0.