dbt-state
dbt plugin that skips redundant model executions by caching results from previous runs
Decision gist · record as of 2026-08-14
Yes, if you run dbt pipelines frequently and want to reduce execution time and compute costs. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. Verify dbt-core compatibility and review how caching integrates with your workflow before deploying to production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.9.0 and dbt-core to be installed; dbt-state acts as a plugin within the dbt ecosystem, not a standalone tool.
- Low install friction with a pure-Python wheel.
- Active maintenance as of 2026-08-14.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 permits commercial use, modification, and redistribution with attribution. You may use this in proprietary projects provided you include the license and document changes.
last release 2026-08-14 (0 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 679,147 downloads/mo, #5,372 on PyPI
Alternatives
Verify before relying
pip install dbt-state
# After installation, dbt-state integrates as a dbt plugin
# and is invoked through dbt commands that reference cached state- How dbt-state integrates with existing dbt workflows and whether it requires configuration changes to dbt_project.yml or profiles.yml.
- Whether the cache is stored locally, remotely, or both, and what storage backends are supported.
- Performance improvement metrics or benchmarks for typical dbt projects.
- Compatibility matrix with specific dbt-core versions and whether breaking changes exist between releases.
What it is and what it does
dbt-state is a plugin for dbt that accelerates data transformation pipelines by caching model execution results and skipping runs that would produce identical outputs. It integrates into the dbt ecosystem and uses prior run state to determine which models need to execute, reducing redundant computation and shortening build times.
The plugin depends on dbt-core and a suite of supporting libraries including protobuf for serialization, grpcio for communication, and query-cache utilities for state management. It is actively maintained and requires Python 3.9 or later. The Apache 2.0 license permits use in commercial and proprietary projects.
Use it for
- Accelerate CI/CD pipelines by skipping model rebuilds when upstream data hasn't changed.
- Reduce cloud data warehouse costs by avoiding redundant SQL execution on unchanged models.
- Speed up local development iteration by caching intermediate model results across dbt runs.
- Enable incremental dbt workflows where only affected downstream models are recomputed after source updates.
- Optimize scheduled dbt jobs by detecting and skipping models with no new input data.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run dbt pipelines frequently and want to reduce execution time and compute costs.
The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. Verify dbt-core compatibility and review how caching integrates with your workflow before deploying to production.
Install
dbt-state on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance as of 2026-08-14. Requires dbt-core and 16 runtime dependencies including protobuf, grpcio, and query-cache libraries; verify compatibility with your dbt version before installing.
Requires Python >=3.9.0 and dbt-core to be installed; dbt-state acts as a plugin within the dbt ecosystem, not a standalone tool.
License in practice
Apache License 2.0 permits commercial use, modification, and redistribution with attribution. You may use this in proprietary projects provided you include the license and document changes.
Quickstart
pip install dbt-state
# After installation, dbt-state integrates as a dbt plugin
# and is invoked through dbt commands that reference cached stateVerify before relying
- How dbt-state integrates with existing dbt workflows and whether it requires configuration changes to dbt_project.yml or profiles.yml.
- Whether the cache is stored locally, remotely, or both, and what storage backends are supported.
- Performance improvement metrics or benchmarks for typical dbt projects.
- Compatibility matrix with specific dbt-core versions and whether breaking changes exist between releases.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 16 packagesaenumauthlibclickdacitedbt-coregrpciohumanizeprotobufpytimeparse2pyyamlquery-cache-commonquery-cache-protobufrequestsrichsqlglottyping-extensions |
| Maintenance | Actively maintained 0 days since the last release |
| First released | |
| Downloads | 679,147 / month, #5,372 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dbt_state-2.43.1-py3-none-any.whl
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