When to Use It
- Explore and inventory your BigQuery project structure
- Find available datasets before building data workflows
- Use it as AI Tool for AI Agents to understand your data organization
Inputs
| Field | Type | Required | Description |
|---|---|---|---|
| Project | Select | Yes | Google BigQuery project to list datasets from |
| Limit | Number | No | Maximum number of datasets to return (1-1000) |
Outputs
| Output | Description |
|---|---|
| Datasets List | List of all datasets with names, IDs, and metadata |
Credit Cost
1 credit per operation.Real-World Example
BigQuery AI Agent:Understanding Dataset Information
The returned data includes: Dataset Metadata:- Dataset ID and name
- Creation time and last modified date
- Location (region) information
- Access permissions and settings
- Understanding data organization
- Planning data architecture
- Identifying unused or outdated datasets
- Managing data governance and compliance
FAQ
What information do I get about each dataset?
What information do I get about each dataset?
You’ll receive dataset ID, name, creation date, last modified date, location, and basic metadata. This helps you understand the structure and organization of your BigQuery project.
Does this show datasets I don't have access to?
Does this show datasets I don't have access to?
No, you’ll only see datasets that your connected Google account has permission to view. Hidden or restricted datasets won’t appear in the results.
Can I use this to create new datasets?
Can I use this to create new datasets?
No, this node only lists existing datasets. You’ll need to create datasets through the BigQuery console or other BigQuery management tools.
How often should I run this for project monitoring?
How often should I run this for project monitoring?
For active projects, weekly or monthly dataset inventory can help with organization. For stable projects, quarterly reviews are usually sufficient.
What if I have many datasets and hit the limit?
What if I have many datasets and hit the limit?
You can increase the limit parameter up to 1000, or run multiple queries with different filters. Consider if you really need all datasets for your specific use case.
How does this help with data governance?
How does this help with data governance?
Regular dataset listing helps track data growth, identify unused datasets, and maintain organized data architecture. Export results to spreadsheets for governance documentation.