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BigQuery Utils is a Google-maintained collection of scripts, user-defined functions (UDFs), dashboards, and notebooks designed to simplify BigQuery migration and data warehouse operations. It includes tools for performance optimization, cross-database function compatibility, and monitoring BigQuery resource usage.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | GoogleCloudPlatform/bigquery-utils |
| Owner | GoogleCloudPlatform |
| Primary language | Jupyter Notebook |
| License | Apache-2.0 — OSI-approved |
| Stars | 1.3k |
| Forks | 332 |
| Open issues | 64 |
| Latest release | Unknown |
| Last updated | 2026-07-03 |
| Source | https://github.com/GoogleCloudPlatform/bigquery-utils |
What bigquery-utils is
The repository provides Jupyter notebooks, SQL/Python/Shell scripts, Looker Studio dashboards, stored procedures, and UDFs (including Apache Datasketches integration) for BigQuery. Migration UDFs emulate proprietary functions from Netezza, Oracle, Redshift, Snowflake, Teradata, Vertica, and SQL Server to ease schema and query porting.
Get the bigquery-utils source
Clone the repository and explore it locally.
git clone https://github.com/GoogleCloudPlatform/bigquery-utils.gitcd bigquery-utils# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Audit which migration UDFs align with your source database dialect and BigQuery query patterns before bulk import; test on sample data first.
- The repository is Jupyter Notebook-heavy; ensure your team can run Colab notebooks or locally execute them in your data environment.
- Performance testing artifacts (JMeter configs) are examples only; customize load profiles and metrics to match your workload characteristics.
- UDFs are modular; selectively deploy only the functions your queries need to avoid schema bloat and dependency management overhead.
- Monitor GitHub issues (64 open at snapshot) for bugs or feature requests relevant to your use case; contribute fixes if needed.
When to avoid it — and what to weigh
- Need Real-Time, Production-Grade Support — This is an unofficial Google resource (explicitly disclaimed as 'not an official Google Product'). It lacks versioned releases and SLA guarantees; critical environments require commercial support channels.
- Require Extensive Backward Compatibility Guarantees — No release versioning strategy is documented. Breaking changes to UDFs or scripts may occur without deprecation periods, risking production pipelines that depend on specific function signatures.
- Building a Standalone, Self-Contained Application — This is a utilities collection, not a framework. It requires existing BigQuery expertise and Google Cloud Platform infrastructure; not suitable if you need a turnkey data warehouse solution.
- Highly Specialized Proprietary Database Migrations — Migration UDFs cover major platforms (Oracle, Snowflake, Redshift, etc.) but may not handle niche proprietary dialects or edge-case function behaviors; verify coverage before committing to this approach.
License & commercial use
Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license that permits commercial use, modification, and distribution with attribution.
Apache 2.0 permits commercial use. However, this is an unofficial, unsupported resource with no warranty or indemnification. Organizations using it in production should review their risk tolerance, conduct internal security/compliance review, and maintain independent support plans. No commercial support arrangement with Google is implied.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
This is an unofficial resource; conduct code review before deploying UDFs or scripts in security-sensitive environments. UDFs run in BigQuery's execution environment; verify they do not introduce SQL injection or data leakage patterns. Dashboards may expose sensitive metadata (slot usage, job details) via Looker Studio; configure access controls accordingly. No security audit or certification is documented.
Alternatives to consider
dbt Core / dbt Cloud
Provides a managed, version-controlled approach to data transformation and migration with a larger ecosystem, commercial support, and native BigQuery adapter. Better for teams prioritizing repeatability and governance.
Dataflow (Apache Beam on GCP)
Purpose-built for ETL/ELT pipelines on Google Cloud with native monitoring and auto-scaling. Suitable if you need orchestrated, streaming, or complex transformations beyond static UDFs and scripts.
Commercial tools offer schema translation, CDC, and validation with SLA support. Better for large-scale heterogeneous migrations requiring managed services and professional services engagement.
Build on bigquery-utils with DEV.co software developers
Review the repository structure, audit the UDFs relevant to your source database, and test in a non-production environment before deployment. Ensure your team reviews the Apache 2.0 license and risk profile for your organization.
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bigquery-utils FAQ
Is this an official Google Cloud product?
Can I use these UDFs in production?
Do the migration UDFs cover my database?
How often is this repository updated?
Build it with a software development company
From first prototype to production, DEV.co delivers custom software development around tools like bigquery-utils. We staff experienced engineers for integrations, web development, and ongoing support across open-source databases and beyond.
Ready to optimize your BigQuery migration?
Review the repository structure, audit the UDFs relevant to your source database, and test in a non-production environment before deployment. Ensure your team reviews the Apache 2.0 license and risk profile for your organization.