bigquery
Easily send data to Big Query
Decision gist · record as of 2026-08-14
No. The package is dormant with no recent maintenance, zero community adoption (0 stars), and minimal documentation. The official google-cloud-bigquery library is actively maintained and widely used; unless this package offers a genuinely simpler API for your specific use case, the official client is a safer choice. High install friction and lack of transparency about its actual capabilities add further risk.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3 or later.
- The package description is minimal; actual usage patterns are not documented in the fact sheet.
- High install friction with no runtime dependencies.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT (permissive), so you can use, modify, and distribute it freely with minimal restrictions.
last release 2025-01-09 (582 days) · last repo commit 2025-01-09
0 known vulnerabilities (OSV.dev, 2026-08-14) · 229,889 downloads/mo, #9,119 on PyPI
Alternatives
Verify before relying
pip install bigquery
import bigquery
# Consult package documentation for usage patterns- What authentication method does the package use for BigQuery (service account, user credentials, default application credentials)?
- Does the package handle schema inference, or must schemas be provided manually?
- What data formats does it support (CSV, JSON, Parquet, etc.)?
- How does it compare in functionality to the official google-cloud-bigquery library?
What it is and what it does
bigquery is a Python wrapper intended to simplify sending data to Google BigQuery. It aims to reduce boilerplate by offering an easier interface than direct BigQuery API calls. The package has no runtime dependencies, meaning it either wraps system tools or relies on pre-installed BigQuery infrastructure.
The project is dormant—no releases or commits for 582 days until a recent commit on 2025-01-09. With zero stars and minimal documentation in the fact sheet, it appears to be a small, personal utility rather than a widely-adopted library. The keywords 'send', 'data', 'bigquery', 'easy' suggest it targets developers who want quick data ingestion without learning the full BigQuery API, but the lack of maintenance and sparse metadata make it difficult to assess its actual capabilities or reliability.
Use it for
- Quick prototyping: loading test or sample data into BigQuery during development without writing full API boilerplate.
- Batch data ingestion: automating regular uploads of data files to BigQuery tables in simple workflows.
- Data pipeline integration: embedding BigQuery writes into Python scripts where simplicity is prioritized over advanced features.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
The package is dormant with no recent maintenance, zero community adoption (0 stars), and minimal documentation. The official google-cloud-bigquery library is actively maintained and widely used; unless this package offers a genuinely simpler API for your specific use case, the official client is a safer choice. High install friction and lack of transparency about its actual capabilities add further risk.
Install
bigquery on PyPI
Before you install
High install friction with no runtime dependencies. The package is dormant—last release was 582 days ago, though the repository remains active with a commit as recent as 2025-01-09. No maintenance activity beyond that point suggests the project is not actively developed.
Requires Python 3 or later. The package description is minimal; actual usage patterns are not documented in the fact sheet.
License in practice
Licensed under MIT (permissive), so you can use, modify, and distribute it freely with minimal restrictions.
Quickstart
pip install bigquery
import bigquery
# Consult package documentation for usage patternsVerify before relying
- What authentication method does the package use for BigQuery (service account, user credentials, default application credentials)?
- Does the package handle schema inference, or must schemas be provided manually?
- What data formats does it support (CSV, JSON, Parquet, etc.)?
- How does it compare in functionality to the official google-cloud-bigquery library?
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3 |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Dormant 582 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 229,889 / month, #9,119 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: bigquery-0.0.45.tar.gz
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See also pandas-gbq · google-cloud-bigquery-storage · gcloud-aio-bigquery · google-cloud-bigquery-datatransfer · google-cloud-bigquery · gcloud-rest-bigquery · bigquery-schema-generator · google-cloud-bigquery-connection · google-cloud-bigquery-logging · bigquery-magics