google-cloud-dataflow-client
Google Cloud Dataflow Client API client library
Decision gist · record as of 2026-08-14
Yes, if you need to manage Dataflow jobs programmatically from Python. The package is actively maintained, carries no known vulnerabilities, has low install friction, and is part of Google's official client library ecosystem. It is not suitable if you only need to write and run Apache Beam pipelines—use the Apache Beam SDK for that instead.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10; Google Cloud Platform authentication must be configured via google-auth (e.g., service account credentials or Application Default Credentials).
- Low friction installation with a pure-Python wheel.
- Actively maintained as of 72 days ago with an active repository status and recent commits.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.
last release 2026-06-03 (72 days) · last repo commit 2026-08-14 · 5,371 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 70,833,451 downloads/mo, #467 on PyPI
Alternatives
Verify before relying
pip install google-cloud-dataflow-client
from google.cloud import dataflow_v1beta1
client = dataflow_v1beta1.FlexTemplatesServiceClient()- Specific Dataflow API methods and capabilities available in version 0.14.0 beyond basic client instantiation.
- Whether this library is suitable for Apache Beam pipeline submission or primarily for job monitoring and management.
- Performance characteristics and rate limits when managing large numbers of Dataflow jobs.
What it is and what it does
This package is Google's official Python client library for interacting with Google Cloud Dataflow, a managed service for running batch and streaming data processing pipelines. It wraps the Dataflow REST API and gRPC services, allowing developers to programmatically create, monitor, and manage Dataflow jobs from Python code.
The library depends on google-api-core, google-auth, grpcio, proto-plus, and protobuf to handle authentication, RPC communication, and protocol buffer serialization. It targets modern Python versions (3.10 and later) and is maintained as part of the larger google-cloud-python monorepo. The package is in Beta development status and carries no known security vulnerabilities.
Use it for
- Automate Dataflow job submission and monitoring as part of a data pipeline orchestration system.
- Build administrative dashboards or CLI tools that query Dataflow job status and metrics.
- Integrate Dataflow job lifecycle management into Python-based ETL frameworks or data platforms.
- Programmatically manage Dataflow templates and flex templates for repeatable data processing workflows.
- Monitor and control multiple Dataflow jobs across different Google Cloud projects from a centralized Python application.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to manage Dataflow jobs programmatically from Python.
The package is actively maintained, carries no known vulnerabilities, has low install friction, and is part of Google's official client library ecosystem. It is not suitable if you only need to write and run Apache Beam pipelines—use the Apache Beam SDK for that instead.
Install
google-cloud-dataflow-client on PyPI
Before you install
Low friction installation with a pure-Python wheel. Actively maintained as of 72 days ago with an active repository status and recent commits.
Requires Python >= 3.10; Google Cloud Platform authentication must be configured via google-auth (e.g., service account credentials or Application Default Credentials).
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions.
Quickstart
pip install google-cloud-dataflow-client
from google.cloud import dataflow_v1beta1
client = dataflow_v1beta1.FlexTemplatesServiceClient()Verify before relying
- Specific Dataflow API methods and capabilities available in version 0.14.0 beyond basic client instantiation.
- Whether this library is suitable for Apache Beam pipeline submission or primarily for job monitoring and management.
- Performance characteristics and rate limits when managing large numbers of Dataflow jobs.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesgoogle-api-coregoogle-authgrpcioproto-plusprotobuf |
| Maintenance | Actively maintained 72 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 70,833,451 / month, #467 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Internet |
Evidence: google_cloud_dataflow_client-0.14.0-py3-none-any.whl
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See also google-cloud-managedkafka · google-cloud-workflows · google-cloud-billing · google-cloud-service-control · google-cloud-storage-control · google-cloud-notebooks · google-cloud-datacatalog · google-cloud-datastream · google-cloud-batch · google-cloud-dialogflow-cx