Open-Source Databases · GoogleCloudPlatform

bigquery-utils

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.

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Source: GitHub — github.com/GoogleCloudPlatform/bigquery-utils

1.3k

GitHub stars

332

Forks

Jupyter Notebook

Primary language

Apache-2.0

License (OSI-approved)

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.

Quickstart

Get the bigquery-utils source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/GoogleCloudPlatform/bigquery-utils.git2cd bigquery-utils3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Database Migration to BigQuery

Use the migration UDFs folder to translate proprietary SQL functions from legacy databases (Oracle, Teradata, Redshift, Snowflake) into BigQuery-compatible equivalents, reducing manual query rewriting during migration projects.

Performance Optimization & Cost Analysis

Leverage the optimization scripts and system tables dashboards to identify slot utilization bottlenecks, job errors, and billing trends; use JMeter examples for load testing BigQuery endpoints.

Monitoring and Audit Logging

Deploy pre-built views over BigQuery INFORMATION_SCHEMA and audit logs to create operational dashboards for understanding query execution patterns, resource consumption, and security audit trails.

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

Security considerations

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.

Software development agency

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?

No. The repository explicitly disclaims itself as 'not an official Google Product.' It is community-supported and lacks SLA guarantees or commercial support.

Can I use these UDFs in production?

Yes, under Apache 2.0. However, conduct code review, test thoroughly, and maintain independent support. No warranty is provided if they fail or behave unexpectedly.

Do the migration UDFs cover my database?

Coverage includes Netezza, Oracle, Redshift, Snowflake, Teradata, Vertica, and SQL Server. If your database is not listed, you may need to implement custom UDFs or use third-party migration tools.

How often is this repository updated?

Active as of July 2026 (last push date). No formal release cycle is documented; updates are continuous. Subscribe to GitHub notifications or check periodically for changes relevant to your use case.

Software development & web development with DEV.co

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