What causes Partial Parse Error?

A dbt package was upgraded and its macro signatures changed, invalidating the cached parse tree. A model file was renamed or moved and the old cache entry still references the old path. A schema.yml definition was changed in a way that conflicts with a previously compiled manifest. The partial_parse.msgpack file was generated by a different dbt version than the currently installed version. Multiple dbt processes wrote to the target/ directory concurrently, corrupting the cache

How do I fix Partial Parse Error?

Delete the target/ directory and re-run: `rm -rf target/ && dbt run` — this forces a full re-parse.. In dbt Cloud, trigger the job with the `--no-partial-parse` flag to bypass the cache for one run.. If the error appeared after a package upgrade, run `dbt deps` to reinstall packages and then clear target/.. Check that only one dbt process is writing to the target/ directory at a time — parallel jobs sharing a target/ directory will corrupt each other's cache.. Pin the dbt-core version in your requirements.txt or pyproject.toml to prevent silent version drift between local and Cloud environments.

Medium severitydbt

Power BI Refresh Error:
Partial Parse Error

What does this error mean?

Dbt encountered an inconsistency between the cached parse state (manifest.json or partial_parse.msgpack) and the current project files. This surfaces after package upgrades, model renames, or changes to schema.yml files.

Common causes

1A dbt package was upgraded and its macro signatures changed, invalidating the cached parse tree
2A model file was renamed or moved and the old cache entry still references the old path
3A schema.yml definition was changed in a way that conflicts with a previously compiled manifest
4The partial_parse.msgpack file was generated by a different dbt version than the currently installed version
5Multiple dbt processes wrote to the target/ directory concurrently, corrupting the cache

How to fix it

1Delete the target/ directory and re-run: `rm -rf target/ && dbt run` — this forces a full re-parse.
2In dbt Cloud, trigger the job with the `--no-partial-parse` flag to bypass the cache for one run.
3If the error appeared after a package upgrade, run `dbt deps` to reinstall packages and then clear target/.
4Check that only one dbt process is writing to the target/ directory at a time — parallel jobs sharing a target/ directory will corrupt each other's cache.
5Pin the dbt-core version in your requirements.txt or pyproject.toml to prevent silent version drift between local and Cloud environments.

Frequently asked questions

Does a partial parse error affect my data?

No — a partial parse error happens before any SQL is compiled. Your warehouse data is unaffected — the error only means dbt could not start its execution phase.

Why does the partial parse error only happen in dbt Cloud and not locally?

dbt Cloud caches parse state between job runs; local runs start fresh. If you use `--no-partial-parse` locally or always clear target/, you won't see it locally even when Cloud does.

How do I prevent partial parse errors from recurring after every package upgrade?

Add `dbt clean` as the first step in your dbt Cloud job (before `dbt deps` and `dbt run`) to remove the target/ directory and ensure a clean parse state each run.

Official documentation: https://docs.getdbt.com/reference/parsing