Google BigQuery

This documentation describes the integration of MindsDB with Google BigQuery, a fully managed, AI-ready data analytics platform that helps you maximize value from your data. The integration allows MindsDB to access data stored in the BigQuery warehouse and enhance it with AI capabilities.

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Prerequisites

Before proceeding, ensure the following prerequisites are met:

1. Install MindsDB locally via Docker or Docker Desktop.
2. To connect BigQuery to MindsDB, install the required dependencies following this instruction.

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Connection

Establish a connection to your BigQuery warehouse from MindsDB by executing the following SQL command:

CREATE DATABASE bigquery_datasource
WITH
   engine = "bigquery",
   parameters = {
      "project_id": "bgtest-1111",
      "dataset": "mydataset",
      "service_account_keys": "/tmp/keys.json"
   };

Required connection parameters include the following:

• project_id: The globally unique identifier for your project in Google Cloud where BigQuery is located.
• dataset: The default dataset to connect to.

Optional connection parameters include the following:

• service_account_keys: The full path to the service account key file.
• service_account_json: The content of a JSON file defined by the service_account_keys parameter.

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Usage

Retrieve data from a specified table in the default dataset by providing the integration name and table name:

SELECT *
FROM bigquery_datasource.table_name
LIMIT 10;

Retrieve data from a specified table in a different dataset by providing the integration name, dataset name and table name:

SELECT *
FROM bigquery_datasource.dataset_name.table_name
LIMIT 10;

Run SQL in any supported BigQuery dialect directly on the connected BigQuery database:

SELECT * FROM bigquery_datasource (

   --Native Query Goes Here
   SELECT *
   FROM t1
   WHERE t1.a IN (SELECT t2.a
                  FROM t2 FOR SYSTEM_TIME AS OF t1.timestamp_column);

);

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Troubleshooting Guide