dbt-core

With dbt, data analysts and engineers can build analytics the way engineers build applications.

dbt-core Permissive license Apache-2.0 Active ★ 13,638 v1.12.2 released 2026-08-12

Install

dbt-core on PyPI

pip

pip install dbt-core

uv

uv add dbt-core

poetry

poetry add dbt-core

Package facts

License	Apache-2.0 (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	25 — agate, click, daff, dbt-adapters, dbt-common, dbt-core-experimental-parser, dbt-extractor, dbt-protos, jinja2, jsonschema, mashumaro, metricflow, networkx, opentelemetry-api, packaging, pathspec, protobuf, pydantic, python-dotenv, pytz, pyyaml, requests, snowplow-tracker, sqlparse, typing-extensions
Maintenance	actively maintained — 1 days since the last release
Last repo commit	2026-08-13
First released	2019-02-14
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: dbt_core-1.12.2-py3-none-any.whl

Development Status :: 5 - Production/StableOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPy

About dbt-core

from the package's own PyPI description — quoted content, verbatim

dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

architecture (image)

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...

Read as markdown · JSON record · Source repository · Homepage

AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

dbt-core is a command-line tool that enables data analysts and engineers to transform data in warehouses using SQL select statements organized as models, with built-in dependency management, documentation, and testing capabilities.

Low install friction with a pure-wheel distribution. Active maintenance (1 day since release) and strong GitHub presence indicate ongoing development and community support.

Apache-2.0 is permissive, allowing commercial use, modification, and redistribution with minimal restrictions—suitable for most organizational and proprietary contexts.

Usage

pip install dbt-core==1.12.2

from dbt.cli.main import main
main(['run'])

Requires Python 3.10 or later; dbt projects also require a configured data warehouse connection to execute transformations.

Verdict: dbt-core is a mature, actively maintained analytics engineering framework with no known vulnerabilities, broad Python version support (3.10–3.14), and permissive licensing. Its 25 runtime dependencies and low install friction make it production-ready for data transformation pipelines.

Needs verification

Whether the 25 runtime dependencies introduce meaningful supply-chain risk or maintenance burden in practice.
Performance characteristics and scalability limits for large dbt projects with hundreds of models.
Specific data warehouse adapters required beyond dbt-core itself for production use.

data transformation sql modelsdbt analytics engineeringsql data warehouse orchestrationdata pipeline testing frameworkanalytics code version controldbt project managementsql model dependencies