apache-airflow-providers-google
Provider package apache-airflow-providers-google for Apache Airflow
Decision gist · record as of 2026-08-14
Yes, if you run Apache Airflow and need to orchestrate Google Cloud or Google Workspace workloads. The package is actively maintained, production-stable, permissively licensed, and has no known vulnerabilities. The 77 runtime dependencies are substantial; verify your environment can accommodate them and that you need the breadth of Google service coverage. If you only need one or two Google services, consider whether lighter-weight alternatives exist before committing to the full provider.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow >=2.11.0 and Python 3.10–3.14; GCP credentials must be configured (typically via GOOGLE_APPLICATION_CREDENTIALS environment variable or Airflow connection).
- Low install friction with a wheel distribution.
- Active maintenance: released 4 days ago with last commit 2026-08-14.
License · maintenance · safety
Apache-2.0 (permissive) — Apache License 2.0 (permissive): you can use, modify, and distribute this package freely in commercial and private projects, provided you include a copy of the license and do not hold the authors liable.
last release 2026-08-10 (4 days) · last repo commit 2026-08-14 · 46,475 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 22,379,335 downloads/mo, #974 on PyPI
Alternatives
Verify before relying
pip install apache-airflow-providers-google
from airflow.providers.google.cloud.operators.bigquery import BigQueryCreateEmptyTableOperator
from airflow import DAG
with DAG('example') as dag:
task = BigQueryCreateEmptyTableOperator(
task_id='create_table',
dataset_id='my_dataset',
table_id='my_table',
schema_fields=[]
)- Whether all 77 runtime dependencies are required for basic use or if subsets can be installed conditionally.
- Specific version compatibility constraints between litellm, ray, and pandas across Python 3.10–3.14 in practice.
- Performance characteristics and latency overhead when orchestrating high-volume Google Cloud operations.
What it is and what it does
This is an Apache Airflow provider package that extends Airflow with operators, hooks, and sensors for orchestrating workflows across Google's service ecosystem. It covers Google Cloud Platform services (BigQuery, Cloud Storage, Pub/Sub, Dataflow, AI Platform, Spanner, Bigtable, and many others), Google Workspace, Google Ads, Google Analytics, Firebase, and Google Marketing Platform. The package is maintained by the Apache Airflow project and released frequently; it is production-stable and widely used.
You install it on top of an existing Airflow deployment to gain access to task operators that interact with Google services. The package handles authentication via google-auth and provides abstractions for common patterns like data loading, model training, and event publishing. With 77 runtime dependencies, it brings in the full Google Cloud SDK ecosystem; this makes it feature-complete but also means your environment must accommodate a large dependency tree.
Use it for
- Schedule and monitor BigQuery data pipelines, transformations, and exports from Airflow DAGs.
- Orchestrate Google Cloud Dataflow (Apache Beam) jobs and monitor their execution.
- Automate data ingestion from Google Cloud Storage, Pub/Sub, or BigQuery into downstream systems.
- Trigger and manage Google AI Platform (Vertex AI) model training and batch prediction jobs.
- Coordinate multi-step workflows spanning Google Ads, Analytics, and Workspace APIs.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you run Apache Airflow and need to orchestrate Google Cloud or Google Workspace workloads.
The package is actively maintained, production-stable, permissively licensed, and has no known vulnerabilities. The 77 runtime dependencies are substantial; verify your environment can accommodate them and that you need the breadth of Google service coverage. If you only need one or two Google services, consider whether lighter-weight alternatives exist before committing to the full provider.
Install
apache-airflow-providers-google on PyPI
Before you install
Low install friction with a wheel distribution. Active maintenance: released 4 days ago with last commit 2026-08-14. Requires Apache Airflow >=2.11.0 and brings 77 runtime dependencies covering Google Cloud SDKs, authentication, and data processing libraries; evaluate whether your environment can accommodate this dependency footprint.
Requires Apache Airflow >=2.11.0 and Python 3.10–3.14; GCP credentials must be configured (typically via GOOGLE_APPLICATION_CREDENTIALS environment variable or Airflow connection).
License in practice
Apache License 2.0 (permissive): you can use, modify, and distribute this package freely in commercial and private projects, provided you include a copy of the license and do not hold the authors liable.
Quickstart
pip install apache-airflow-providers-google
from airflow.providers.google.cloud.operators.bigquery import BigQueryCreateEmptyTableOperator
from airflow import DAG
with DAG('example') as dag:
task = BigQueryCreateEmptyTableOperator(
task_id='create_table',
dataset_id='my_dataset',
table_id='my_table',
schema_fields=[]
)Verify before relying
- Whether all 77 runtime dependencies are required for basic use or if subsets can be installed conditionally.
- Specific version compatibility constraints between litellm, ray, and pandas across Python 3.10–3.14 in practice.
- Performance characteristics and latency overhead when orchestrating high-volume Google Cloud operations.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 77 packagesapache-airflowapache-airflow-providers-common-compatapache-airflow-providers-common-sqlasgirefdillgcloud-aio-authgcloud-aio-bigquerygcloud-aio-storagegcsfsgoogle-adsgoogle-analytics-admingoogle-api-coregoogle-api-python-clientgoogle-authgoogle-auth-httplib2google-genaigoogle-cloud-aiplatformtqdmscikit-learnjsonschemaruamel.yamlpyyamllitellmraygoogle-cloud-bigquery-storagegoogle-cloud-alloydbgoogle-cloud-automlgoogle-cloud-bigquerygoogle-cloud-bigquery-datatransfergoogle-cloud-bigtable |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 22,379,335 / month, #974 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/StableEnvironment :: ConsoleEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersIntended Audience :: System AdministratorsProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: System :: Monitoring |
Evidence: apache_airflow_providers_google-22.3.0-py3-none-any.whl
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See also apache-airflow-providers-amazon · apache-airflow-providers-apache-hive · apache-airflow-providers-microsoft-azure · apache-airflow-providers-oracle · apache-airflow-providers-cncf-kubernetes · apache-airflow-providers-postgres · apache-airflow-providers-apache-cassandra · apache-airflow-providers-databricks · apache-airflow-providers-common-messaging · apache-airflow-providers-mysql