dbt-duckdb
The duckdb adapter plugin for dbt (data build tool)
Decision gist · record as of 2026-08-14
Yes. dbt-duckdb is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It is a solid choice if you want to run dbt transformations against an embedded or cloud-hosted DuckDB instance, especially for local development, testing, or lightweight analytics workloads that read/write external files directly.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires dbt-core >= 1.8.x and duckdb >= 1.0.0; Python >= 3.10.
- Low friction: pure Python wheel with four runtime dependencies (dbt-core, dbt-adapters, dbt-common, duckdb).
- Actively maintained with a release 7 days ago and 1334 repository stars.
License · maintenance · safety
Apache-2 (permissive) — Apache-2 permissive license: you can use, modify, and distribute dbt-duckdb freely in commercial and private projects, with minimal restrictions.
last release 2026-08-07 (7 days) · last repo commit 2026-08-12 · 1,334 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,932,864 downloads/mo, #3,423 on PyPI
Alternatives
Verify before relying
pip install dbt-duckdb
# In profiles.yml:
default:
outputs:
dev:
type: duckdb
path: /tmp/dbt.duckdb
target: dev
# Then run dbt commands as usual:
dbt run- Whether the package supports all DuckDB extensions listed in the description (httpfs, parquet, h3, uc_catalog, etc.) in practice.
- Performance characteristics and scalability limits for typical data lakehouse workloads.
- Compatibility with MotherDuck and DuckLake features beyond what the description states.
What it is and what it does
dbt-duckdb is an adapter that integrates dbt's data transformation framework with DuckDB, an embedded OLAP database designed for analytics. It lets you write dbt models in SQL or Python and execute them against DuckDB, which can read and write data directly from CSV, JSON, and Parquet files without requiring a separate load step. This enables a lightweight, self-contained data stack suitable for local development, CI/CD testing, or building a data lakehouse with minimal infrastructure.
The adapter supports both in-memory and persistent database modes, DuckDB extensions (like httpfs for cloud storage), the DuckDB Secrets Manager for credential handling, and fsspec-based filesystems for S3, GCS, and Azure Blob Storage. It also integrates with MotherDuck for cloud-hosted DuckDB instances and supports persisting dbt documentation as database comments. The package is actively maintained, supports Python 3.10–3.13, and has no known vulnerabilities.
Use it for
- Test dbt data pipelines locally or in CI jobs using an in-memory DuckDB database without external dependencies.
- Build a lightweight data lakehouse that transforms CSV, Parquet, and JSON files directly without loading them into a database first.
- Run analytics transformations against cloud storage (S3, GCS, Azure) using DuckDB extensions and fsspec integration.
- Develop and prototype data pipelines on a single machine before deploying to a larger data warehouse.
- Persist dbt model and column documentation as DuckDB comments for exploration and automated tooling.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
dbt-duckdb is actively maintained, has no known vulnerabilities, low install friction, and a permissive license. It is a solid choice if you want to run dbt transformations against an embedded or cloud-hosted DuckDB instance, especially for local development, testing, or lightweight analytics workloads that read/write external files directly.
Install
dbt-duckdb on PyPI
Before you install
Low friction: pure Python wheel with four runtime dependencies (dbt-core, dbt-adapters, dbt-common, duckdb). Actively maintained with a release 7 days ago and 1334 repository stars.
Requires dbt-core >= 1.8.x and duckdb >= 1.0.0; Python >= 3.10.
License in practice
Apache-2 permissive license: you can use, modify, and distribute dbt-duckdb freely in commercial and private projects, with minimal restrictions.
Quickstart
pip install dbt-duckdb
# In profiles.yml:
default:
outputs:
dev:
type: duckdb
path: /tmp/dbt.duckdb
target: dev
# Then run dbt commands as usual:
dbt runVerify before relying
- Whether the package supports all DuckDB extensions listed in the description (httpfs, parquet, h3, uc_catalog, etc.) in practice.
- Performance characteristics and scalability limits for typical data lakehouse workloads.
- Compatibility with MotherDuck and DuckLake features beyond what the description states.
Package facts
| License | Apache-2 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesdbt-commondbt-adaptersduckdbdbt-core |
| Maintenance | Actively maintained 7 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,932,864 / month, #3,423 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: dbt_duckdb-1.11.0-py3-none-any.whl
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See also duckdb · duckdb-extension-httpfs · duckdb-engine · duckdb-extensions · dbt-athena · dbt-athena-community · dlt · dbt-core · dbt-databricks · dbfread