Registry / devops / apache-airflow-providers-dbt-cloud

apache-airflow-providers-dbt-cloud

library4.9.3pypypi✓ verified 23d ago

This provider package allows Apache Airflow to interact with dbt Cloud, enabling orchestration of dbt Cloud jobs and fetching job run details. It includes operators, sensors, and hooks for various dbt Cloud functionalities. The current version is 4.8.0. Airflow provider packages typically follow a regular release cadence, often aligned with Airflow's own releases or as new features/bug fixes are introduced.

devops workflow

pip install apache-airflow-providers-dbt-cloud

github ↗package ↗docs ↗changelog ↗

INSTALL

IMPORT

SIG · APACHE-AIRFLOW-PRO

apache-airflow-providers-dbt-cloud

devopspythonv4.9.3

Install

26.4s avg

Import

—

Disk

267MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v4.9.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.000s · 265.8MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 26.4s · import 0.000s · 266MB

267MB installed

● package 267MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

DbtCloudHook

✓ from airflow.providers.dbt.cloud.hooks.dbt_cloud import DbtCloudHook

✗ from airflow.providers.dbt.cloud.hooks.dbt_cloud import DbtCloudHook

This example DAG demonstrates how to trigger a dbt Cloud job and then wait for its completion using the `DbtCloudRunJobOperator` and `DbtCloudJobRunSensor`. It uses deferrable operators for efficient async execution. Ensure you configure an Airflow connection of type 'dbt Cloud' named `dbt_cloud_default` (or your chosen `DBT_CLOUD_CONN_ID`) with your dbt Cloud API Token. Also, provide your `account_id` and `job_id`.

from __future__ import annotations import os import pendulum from airflow.models.dag import DAG from airflow.providers.dbt_cloud.operators.dbt_cloud import DbtCloudRunJobOperator from airflow.providers.dbt_cloud.sensors.dbt_cloud import DbtCloudJobRunSensor DBT_CLOUD_CONN_ID = os.environ.get('DBT_CLOUD_CONN_ID', 'dbt_cloud_default') DBT_CLOUD_ACCOUNT_ID = os.environ.get('DBT_CLOUD_ACCOUNT_ID', '12345') # Your dbt Cloud Account ID DBT_CLOUD_JOB_ID = os.environ.get('DBT_CLOUD_JOB_ID', '67890') # Your dbt Cloud Job ID with DAG( dag_id="dbt_cloud_example_dag", schedule=None, start_date=pendulum.datetime(2023, 1, 1, tz="UTC"), catchup=False, tags=["dbt_cloud", "example"], ) as dag: trigger_dbt_cloud_job = DbtCloudRunJobOperator( task_id="trigger_dbt_cloud_job", dbt_cloud_conn_id=DBT_CLOUD_CONN_ID, account_id=DBT_CLOUD_ACCOUNT_ID, job_id=DBT_CLOUD_JOB_ID, check_interval=10, # Check job status every 10 seconds timeout=60 * 20, # Fail after 20 minutes deferrable=True # Enable deferrable mode for async execution ) wait_for_dbt_cloud_job = DbtCloudJobRunSensor( task_id="wait_for_dbt_cloud_job", dbt_cloud_conn_id=DBT_CLOUD_CONN_ID, account_id=DBT_CLOUD_ACCOUNT_ID, job_id=DBT_CLOUD_JOB_ID, deferrable=True ) trigger_dbt_cloud_job >> wait_for_dbt_cloud_job

Debug

Known issues

breakingVersion 4.0.0 removed all deprecated parameters in operators and hooks. If you were using any parameters marked as deprecated in previous versions (e.g., `schema`, `project_id`, `environment_id`), your DAGs will break.

fix

Review the latest documentation for the DbtCloud provider and update your DAGs to use the current parameter names and structures. For instance, ensure `account_id` and `job_id` are explicitly passed.

affects: >=4.0.0

breakingIn version 3.0.0, the `poll_interval` parameter was removed from `DbtCloudRunJobOperator` as it was only supported in `DbtCloudJobRunSensor`. Additionally, the `deferrable` parameter was added to `DbtCloudRunJobOperator` for async execution.

fix

Remove `poll_interval` from `DbtCloudRunJobOperator` instances. To leverage deferrable mode, set `deferrable=True` on both operators and sensors. If you need a specific polling interval for the run, set it on `DbtCloudJobRunSensor` or use `check_interval` on `DbtCloudRunJobOperator`.

affects: >=3.0.0

gotchaIncorrect or missing dbt Cloud Connection configuration. The provider requires an Airflow connection of type 'dbt Cloud' with a valid API token. Common issues include using the wrong connection ID, an expired token, or a token with insufficient permissions.

fix

Verify that your Airflow connection (e.g., `dbt_cloud_default`) is correctly set up with the 'dbt Cloud' type and a valid, unexpired API token. Ensure the API token has the necessary read/write permissions for the specific dbt Cloud account and jobs you are interacting with.

affects: All

gotchaConfusion between `account_id`, `job_id`, and other identifiers. Users often provide incorrect IDs for dbt Cloud resources, leading to 'resource not found' or 'permission denied' errors.

fix

Double-check that you are passing the correct `account_id` and `job_id` for your dbt Cloud environment. These can be found in the dbt Cloud UI (e.g., in the URL when viewing a job or account settings).

affects: All

Upgrade

Version history

4.9.3latest on PyPI · released Aug 8, 2026

Audit

Dependencies

apache-airflowrequiredThis is an Airflow provider, requiring Apache Airflow (>=2.4.0) to function.

dbt-cloud-sdkrequiredUsed internally by the provider for dbt Cloud API interactions.

Agent activity

41 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/apache/airflow ↗

packagehttps://pypi.org/project/apache-airflow-providers-dbt-cloud/ ↗

docshttps://airflow.apache.org/docs/apache-airflow-providers-dbt-cloud/4.8.2 ↗

changeloghttps://airflow.apache.org/docs/apache-airflow-providers-dbt-cloud/4.8.2/changelog.html ↗