Google BigQuery is one of the most powerful Data Warehouses in the world. Used by businesses big and small, Google BigQuery is a serverless, cost-effective data management solution that allows businesses to scale according to their needs. It is a fully managed data enterprise solution that comes with a variety of built-in tools, ranging from geospatial analysis to machine learning and business intelligence too.

However, the analytical capabilities of Google Bgquery are obviously not as in-depth as standalone BI tools, which is one of the main reasons why so many people prefer connecting apps like Metabase to their Google BigQuery datasets. The serverless architecture that Google BigQuery leverages allows organizations to easily run SQL queries and get answers to some of the organization’s most pressing questions. 

However, what if you want to set up a Google BigQuery Metabase Integration? Metabase is a popular business intelligence tool that you can use in order to get better insights into your data. It allows you to visualize your data and generate comprehensive reports instead of having to worry about running SQL queries. This article will introduce you to Google BigQuery and Metabase. It will further explain the steps using which you can easily set up your Google BigQuery Metabase Integration. Read along to learn the steps and benefits of this connection!

What is Google BigQuery

BigQuery Metabase: Google BigQuery Logo
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Google BigQuery Data Warehouse is a famous cloud-based platform that provides flexible and scalable storage. This fully functional Data Warehouse is acknowledged for its high-speed Data Analytics and query processing offerings that can operate on vast datasets easily. It is capable of running SQL Queries on billions of rows in a single go. Moreover, Google BigQuery automates the task of resource allocation. Its columnar structure facilitates seamless querying and Data Aggregation processes. Furthermore, this platform is highly secure and empowers you to test the identification of your clients before providing them access to the data.

To explore more about Google BigQuery, click here.

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What is Metabase

BigQuery Metabase: Metabase Logo
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Metabase is an online high-level Data Analytics tool that enables you to extract valuable insights from your vast set of data. The main benefit of Metabase is that you don’t need to write any code or SQL query to get the desired results. You can seamlessly use the in-built filters and read through to data to generate various summaries and reports. Moreover, it enables you to develop charts and tables using which you can upgrade your Data Visualization.

In case you are dealing with complex datasets and the in-built filters are not sufficient to cope up, you can use queries and transform the data into easy to understand form. Furthermore, you may customize Metabase to alert all the customers if the data goes out of control.

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Steps to Set Up Google BigQuery Metabase Integration

You can easily set up your Google BigQuery Metabase Integration using the following steps:

Step 1: Create a Metabase Account

The first step in your Google BigQuery Metabase Integration is to create an account on Metabase. Just log on to the official website and then follow the steps to quickly sign up as shown in the below image. Metabase also offers a 14-day free trial. 

BigQuery Metabase: Sign up Metabase
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Once you create an account, you can then add a new database. 

Step 2: Add a Database Connection

Your Google BigQuery Metabase Integration will operate on a robust database connection. To add a database connection, go to the Databases tab that’s in the Admin section. You can find all of your connected databases. Since there isn’t any, just click on Add database to connect your Google BigQuery database. 

Before you can start using the Google BigQuery Metabase connection, it is important that you create an account on the Google Cloud Platform. You also have to link it to a project that you are going to use. The project should have a BigQuery dataset that Metabase can seamlessly connect with. 

More importantly, you also need to set up a service account JSON file that Metabase is required to access the Google BigQuery dataset. Service accounts are generally designed for use with applications, including Metabase. 

To create a service account for your BigQuery Metabase Integration, here are the steps that you need to follow:

  • Open your Google Cloud Platform project console, and then go to the IAM & Admin section. From there, just select Service account. Just click on CREATE SERVICE ACCOUNT to get started.
  • From there, you will have to provide the service account details and then select the Create button. The service account will also need to be given access to the project, and you will have to assign roles t the service account accordingly. This is necessary because Metabase requires permissions to the service account in order to run queries and view them against the dataset. 

You will have to add the following roles to ensure Metabase runs smoothly:

  • Data Viewer
  • Metadata Viewer
  • Job User

Once you’re done, the next step is to create the key for your Google BigQuery Metabase Integration. When you press the button, it’ll ask you for the key type. Select JSON, and the file download will start to your computer. 

Note: The key will download only once to your computer. In case the file gets lost or deleted, a separate service account will have to be created with the same roles. 

Step 3: Connect Google BigQuery Dataset to MySQL Account

Once you have the JSON file downloaded on your computer for the specific Google BigQuery dataset, just go to your Metabase account, and click on “settings“. From there, go to “Admin” and then select “Databases“. Now, you can click on “Add database” to establish the Google BigQuery Metabase connection.

From the Database type dropdown menu, click on “BigQuery“. Since this database is officially supported, Metabase will automatically optimize the required configuration settings. Here’s how it looks: 

BigQuery Metabase: Keys in Metabase
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Step 4: Fill Out Key Settings

Now, there are several essential key settings that you should know about to optimize your Google BigQuery Metabase Integration. Let’s run through them one by one.

  • Name: This is the title of the database that you create in Metabase. Since Metabase keeps its own set of tables and columns, you can choose a unique identifier that links to your Google BigQuery dataset. 
  • Dataset ID: Every dataset that you connect from Google BigQuery will have its own Dataset ID. This can be found on the Google Cloud Console interface. It’s important for your BigQuery Metabase setup to work that you avoid adding the Project ID prefix that’s added by default in BigQuery datasets. 
  • Uploading the Service Account JSON File: The next step is to upload the service account JSON file that you downloaded earlier. The JSON file is necessary as it includes the key credentials required by the Metabase application to run queries and view the data. Make sure you also add the necessary roles, though you can always add any extra roles once the Google BigQuery Metabase connection is in place. 
  • Automatically Run Queries When Doing Simple Filtering and Summarizing: By default, this option is enabled. The toggle simply indicates that Metabase will run queries automatically when a user does simple explorations with the help of the Filter and the Summarize option, especially when viewing tables or charts. You can always turn this setting off if you feel that it’s taking a toll on the Google BigQuery Metabase’s database timings. The setting also doesn’t have an impact on your SQL queries, so it primarily depends on the size of the database. 
  • Let Metabase Choose When to Sync and Scan: This last option is primarily reserved for larger databases that take a longer time to sync and scan. By default, this option is turned on, which means that Metabase automatically runs an hourly sync and a more thorough scan on a daily basis to update any field value changes. 

If you have a larger database, this setting should remain on so you can get a better understanding of your Biguery Metabase Integration data, as soon as new field values are updated. 

Step 5: Save the Key Settings

This is the final step in your Google BigQuery Metabase Integration. Once you have enabled all the options and selected the settings, the next step is to save your configuration. Just click on “Save“, and your new Google BigQuery database will be connected to your account. Metabase will also confirm that the new database has been added as shown in the below image. 

BigQuery Metabase: Database Added
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On top of that, Metabase often adds some automatic data explorations, so you can just click on Explore this data to check out any new explorations. Or, if you want, just click on the button on the left, and you can further check out the Admin panel. 

Keep in mind that data syncing is obviously not immediate. It takes a bit of time for Metabase to sync with the dataset in your Google BigQuery database. Once it’s done, you can just move out of the Admin section and then click on Browse Data to view all of your data in Metabase. 

That’s it! Your Google BigQuery Metabase Integration is in place.

Benefits of Google BigQuery Metabase Integration

The Google BigQuery Metabase Integration adds the following benefits to your business:

  • This Google BigQuery Metabase connection allows organizations to gain unprecedented insights into the way they manage their data and learn new things. The distributed analysis engine that Google BigQuery is known for is also capable of easily querying terabytes of data within a matter of seconds. Even if you want to query petabytes, it takes a few minutes at most.
  • Google BigQuery is focused on flexibility and agility. Since it’s owned by Google, BigQuery uses the Google Cloud Console interface, and there’s also the command-line tool that you can use. Now, pairing it up with Metabase will allow you to focus more on the analytical aspect of your data rather than infrastructure and support. Moreover, you will be in a better position to make data-driven decisions using the insights gathered by Metabae from your Google BigQuery’s data.
  • Any person who’s familiar with popular programming languages such as Python, Javascript, Go, and Java will find themselves at home with Google BigQuery. Moreover, since Metabase requires an easy-learning curve anyone can familiarize with the Google BigQuery Metabase Integration within a short span of time.


The article introduced you to Google BigQuery and Metabase. It further explained the detailed steps using which you can set up your Google BigQuery to Metabase Integration. The article also listed the important benefits that you can get by connecting Google BigQuery to Metabase.

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Share your understanding of BigQuery Metabase Integration in the comments below!

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