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About Google BigQuery

Google BigQuery is a fully managed, serverless data warehouse and analytics platform offered by Google Cloud. Key features include:
  • Serverless Architecture: BigQuery eliminates the need for infrastructure management, allowing users to focus solely on data analysis.
  • Scalability: It can process petabytes of data quickly, making it suitable for large-scale analytics.
  • Built-in Machine Learning: With BigQuery ML, users can create and execute machine learning models directly within BigQuery using SQL, facilitating predictive analytics without extensive ML expertise.
  • Real-time Analytics: BigQuery supports real-time data analysis, enabling timely insights for decision-making.
  • Integration with Google Cloud Ecosystem: It seamlessly integrates with other Google Cloud services, such as Cloud Storage, Dataflow, and Looker Studio, enhancing data processing and visualization capabilities.

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Authentication Options

Below are possible authentication options you can choose:
The simplest way to connect to Google BigQuery is by using xpander.ai’s built-in authentication:
  1. Go to the Connectors section in the sidebar of your xpander.ai dashboard.
  2. Select BigQuery from the available integrations.
  3. Click Sign in with BigQuery.
  4. Grant xpander.ai permission to access your account.
  5. Your Google BigQuery integration is now ready to use.

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Integration of Google BigQuery into AI Agent

Once you’ve configured your Google BigQuery account with the authentication option(s) described above, you can integrate it into your AI agent with xpander.ai:
  1. Go to the + sign located in the top right of the graph visualization of your xpander.ai agent.
  2. Select Connectors.
  3. Choose BigQuery with the same Interface name you configured in the previous section (e.g., xpander-bigquery).
  4. Select the available Google BigQuery operations that suit your use case.
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Expose Google BigQuery as MCP Server

Alternatively, you can also expose your Google BigQuery account as an MCP server. To do so:
  1. Go to the Connectors section in the sidebar of your xpander.ai dashboard.
  2. Select BigQuery with the same Interface name you configured in the previous section (e.g., xpander-bigquery).
  3. Click MCP Configuration.
  4. Enter the MCP configuration into the appropriate settings of the client app you want to use (e.g., Cursor, Windsurf, Claude Desktop, etc.).
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AI Agent Google BigQuery Prompt Library

Below are possible prompts or use cases you can try after integrating Google BigQuery into your xpander AI agent: 
Could you show me all datasets within the {project_name} project?

Can you create a new dataset called {dataset_name} in our {project_name} project with a 30-day expiration policy?

I need detailed information about the {dataset_name} dataset in our {project_name} project. Can you retrieve that?

Can you run this SQL query "{query_string}" on our {project_name} project using the {dataset_name}?