> 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/administer-and-govern/control/connections/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 Control → 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.lleverage.ai/administer-and-govern/control/connections/google-bigquery.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.