# Integrate Google BigQuery with Databox Google BigQuery is a fully managed, serverless data warehouse built to analyze large volumes of structured and semi-structured data at scale. It eliminates infrastructure management by automatically handling storage, performance, and scaling, allowing teams to focus on querying and insights instead of maintenance. BigQuery supports GoogleSQL, real-time analytics, and native integrations across the Google Cloud ecosystem. It is commonly used for centralized data analysis, event tracking, product analytics, and powering business intelligence tools with fast, reliable reporting. ## Connection ## Step 1: Create a service account To allow Databox to securely access your BigQuery data, create a dedicated service account in Google Cloud. 1. Go to [IAM & Admin > Service Accounts.](https://console.cloud.google.com/iam-admin/serviceaccounts) 2. Click **+ Create Service Account**. 3. Enter a **Service account name** (e.g., “Databox Integration”). 4. Review the auto-generated **Service account ID**. You can edit it if needed. 5. Click **Create and continue**. create-service-account [Official Documentation](https://docs.cloud.google.com/iam/docs/service-accounts-create) ### Step 2: Grant permissions Assign the necessary roles so the service account can read data and run queries in BigQuery. 1. Grant the **BigQuery Data Viewer** and **BigQuery User** roles to the new service account. 2. Click **Continue**. 3. Grant access to users or groups that need to perform actions as the service account (optional). 4. Click **Done**. grant-permission [Official Documentation](https://docs.cloud.google.com/iam/docs/manage-access-service-accounts) ### Step 3: Generate credentials Create a JSON key file that Databox will use to authenticate the connection. 1. In the [Service accounts](https://console.cloud.google.com/iam-admin/serviceaccounts) page, click the service account you just created. 2. Go to the **Keys** tab. 3. Click **Add key** and select **Create new key**. 4. Choose **JSON** as the key type. 5. Click **Create** to download the JSON file generate-credentials Store this file securely. You will need its contents in the next step. [Official Documentation](https://docs.cloud.google.com/iam/docs/keys-create-delete) ### Step 4: Enter your Google BigQuery connection details in Databox Next, add the BigQuery connection in Databox using the credentials you generated. - **Name of the data source** (required) — A label used to identify the BigQuery connection in Databox. - **Credential file contents** (required) — The full contents of the JSON key file generated in [Step 3](#step-3-generate-credentials). - **SQL Mode** — GoogleSQL is used by default**.** Select the **Use Legacy SQL** checkbox only if your queries require Legacy SQL syntax. - **Time zone** (required) — The time zone used for interpreting and filtering data points in visualizations. enter-credentials ## Datasets The Google BigQuery integration supports the creation of [datasets](/understanding-datasets), which allow you to define and shape the specific data you want to use for reporting in Databox. Datasets make it easier to focus on the most relevant information, enabling you to filter, visualize, and analyze metrics across projects, teams, and clients without writing complex queries each time. ### Steps to create a dataset: 1. **Select a dataset**: Choose the BigQuery dataset that contains the data you want to work with. 2. **Select a table**: Pick the appropriate table within that dataset. 3. **Select columns**: Browse and select the specific columns (fields) from your tables or views to include in your dataset. These columns define the structure and content of your dataset. #### Datasets stored outside the US region If your BigQuery data is stored outside the default **US region** (for example, in the EU region), the dataset list may not populate in the dataset builder. This happens because Databox currently retrieves schema metadata from the US region by default. As a workaround, you can create your dataset using a [custom query](#optional-write-sql) instead of selecting a dataset and table from the list. When querying your tables or views, include the appropriate **region qualifier** in the project reference. For example: ```sql SELECT catalog_name, schema_name FROM `.region-.INFORMATION_SCHEMA.SCHEMATA` ORDER BY schema_name ``` Replace `` with your Google Cloud project ID and `` with the region where your dataset is hosted (for example, `eu`). ### Optional: Write SQL For more advanced use cases, you can write a **custom SQL query** instead of selecting columns manually. This allows you to: - Join multiple tables - Apply filters and aggregations - Format or transform data before importing it into Databox Your query must return a valid tabular result to be used as a dataset. ### Additional resources - [BigQuery GoogleSQL Query Syntax](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax) — Learn how to write queries using GoogleSQL, including SELECT statements, joins, filtering, grouping, and date functions. - [BigQuery Legacy SQL Syntax, Functions and Operators](https://docs.cloud.google.com/bigquery/docs/reference/legacy-sql) — Reference guide for working with older query formats and understanding available operators and built-in functions. ## Resources For comprehensive details on metrics, data availability, templates, specifications, usage guidelines, and other key information, refer to the resources listed below. In 2025, we introduced **dataset support** for Google BigQuery. You can now import raw data from your Google BigQuery account into a reusable [dataset](/create-a-dataset) and build multiple metrics from it using the [metric builder](/create-a-custom-metric)— no need to write separate queries for each metric. This update also removes per-query size limits. Instead, your data usage is governed by Databox's global dataset size limits, giving you more flexibility when working with larger datasets.   Ask Genie Get instant answers or help with your data using the in-app AI assistant. Talk to an expert For customers: Get help with your setup, strategy, or making the most of Databox. Book a demo New to Databox? See how it works and get guidance on getting started. Send an email Reach out to support for help with your account, data, or technical issues.