> For the complete documentation index, see [llms.txt](https://docs.lleverage.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lleverage.ai/external-apps-integration/google-bigquery.md). # Google BigQuery Google BigQuery integration in Lleverage connects your workflows and agents to your data warehouse, so they can look up datasets and run queries, bringing analytics data into your automated processes. ### How to Connect 1. **Go to Settings > Connections** in your project 2. **Pick Google BigQuery** from the list of apps 3. **Choose which actions** the connection should allow and who should have access (shared or personal) 4. **Choose "Connect Now"** and sign in with Google, or send an authentication link so a colleague can connect on your behalf ### What Google BigQuery Can Do * **Browsing** - list the datasets in your project and the tables within a dataset * **Querying** - run a query and use the results in the rest of your workflow > 💡 **Use case**: Pull the latest figures into a scheduled report, let an agent answer data questions with a live query, or check a metric before a workflow decides what to do next. ### Important Considerations * The signed-in Google account's access in BigQuery determines which datasets and tables are available * Queries run against your BigQuery project and count towards its usage, so keep scheduled queries efficient --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.lleverage.ai/external-apps-integration/google-bigquery.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.