google-cloud-dataproc

Google Cloud Dataproc API client library

google-cloud-dataproc Permissive license Apache-2.0 Active ★ 5,370 v5.30.0 released 2026-07-08

Install

google-cloud-dataproc on PyPI

pip

pip install google-cloud-dataproc

uv

uv add google-cloud-dataproc

poetry

poetry add google-cloud-dataproc

Package facts

License	Apache-2.0 (permissive)
Python support	supports the current Python release (>=3.10)
Install friction	low — pure-Python wheel
Runtime dependencies	6 — google-api-core, google-auth, grpcio, proto-plus, protobuf, grpc-google-iam-v1
Maintenance	actively maintained — 36 days since the last release
Last repo commit	2026-08-13
First released	2018-01-22
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: google_cloud_dataproc-5.30.0-py3-none-any.whl

Development Status :: 5 - Production/StableIntended 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

About google-cloud-dataproc

from the package's own PyPI description — quoted content, verbatim

Python Client for Cloud Dataproc

Cloud Dataproc_: Manages Hadoop-based clusters and jobs on Google Cloud Platform.

Client Library Documentation_
Product Documentation_

.. |stable| image:: https://img.shields.io/badge/support-stable-gold.svg :target: https://github.com/googleapis/google-cloud-python/blob/main/README.rst#stability-levels .. |pypi| image:: https://img.shields.io/pypi/v/google-cloud-dataproc.svg :target: https://pypi.org/project/google-cloud-dataproc/ .. |versions| image:: https://img.shields.io/pypi/pyversions/google-cloud-dataproc.svg :target: https://pypi.org/project/google-cloud-dataproc/ .. _Cloud Dataproc: https://cloud.google.com/dataproc .. _Client Library Documentation: https://cloud.google.com/python/docs/reference/dataproc/latest/summary_overview .. _Product Documentation: https://cloud.google.com/dataproc

Quick Start

In order to use this library, you first need to go through the following steps:

Select or create a Cloud Platform project._
Enable billing for your project._
Enable the Cloud Dataproc._
Set up Authentication._

.. _Select...

Read as markdown · JSON record · Source repository · Homepage

AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

Python client library for managing Hadoop-based clusters and jobs on Google Cloud Dataproc, providing programmatic access to create, configure, and monitor data processing infrastructure.

Low friction: pure Python wheel with six well-maintained Google Cloud dependencies (google-api-core, google-auth, grpcio, proto-plus, protobuf, grpc-google-iam-v1). Active maintenance with last commit 2026-08-13 and 36 days since latest release.

Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes, but derivative works are permitted.

Usage

pip install google-cloud-dataproc

from google.cloud import dataproc_v1

client = dataproc_v1.ClusterControllerClient()
# Use client to manage Dataproc clusters

Requires Python >= 3.10; Google Cloud authentication credentials must be configured (via GOOGLE_APPLICATION_CREDENTIALS environment variable or Application Default Credentials).

Verdict: Production-ready, actively maintained library with stable API (Development Status 5), no known vulnerabilities, and low install friction. Suitable for production workloads managing Dataproc infrastructure on GCP, though it requires proper authentication setup and Python 3.10+.

Needs verification

Whether the library supports all current Dataproc API features or if there are known gaps relative to the REST API.
Performance characteristics and rate-limiting behavior when managing large numbers of clusters or jobs.
Whether gRPC connection pooling and retry behavior are configurable for production reliability.

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