Use Google Cloud BigQuery as Data Source in Power BI

access_time 3 years ago visibility10082 comment 8

BigQuery is Google’s serverless data warehouse in Google Cloud. Power BI can consume data from various sources including RDBMS, NoSQL, Could, Services, etc. It is also easy to get data from BigQuery in Power BI.

In this article, I am going to demonstrate how to connect to BigQuery to create visuals.


Google Cloud account is required. You can register a trial account.

In BigQuery, there is a public dataset named world_bank_intl_debt in project bigquery-public-data. We are going to use table international_debt to create some visual.

The details about this table is available here:

Cost of querying public data sets

Public data sets are paid by Google for storage but you need to pay for querying it.

Connect to BigQuery in Power BI

Open Power BI and create a new file.

In the Home tab and click Get Data button.

In the Database tab of the opened window, select “Google BigQuery”.


Click Connect button to continue.

Click Sign in button to sign into your Google Could account.


In the opened window, click Allow button to allow Power BI Desktop to view and manage your data in Google BigQuery:


Click connect button once signed in to continue.

Select the Required Data Tables

The hierarchy of BigQuery is: Project -> DataSet -> Table.

In the opened window Navigator, expand bigquery-public-data project.


For this tutorial, we just need international_debt table under world_bank_intl_debt dataset.


Click Load button to load the data.

And then you can setup Connection settings. In this case, let’s choose Import which will bring a copy of the data into Power BI.

Please note you will pay for querying the data. There are 1,359,644 records in this table. You can customize the query to only retrieve sample data to reduce the cost.


Once imported, the following fields are available to use:


Create a visual using the data imported

With the data available, we can now easily create a line chart by using field year as Axis and field value as Values.


You can create as many visuals as you can do with any other data sources.



It is very easy to consume Google BigQuery data in Power BI. You can create joins when drafting the queries or implement within Power BI.

For performance and cost consideration, you may choose to physicalise some data in BigQuery and then query the aggregated data into Power BI.

info Last modified by Raymond 10 months ago copyright This page is subject to Site terms.
Like this article?
Share on

Please log in or register to comment.

account_circle Log in person_add Register

Log in with external accounts

Follow Kontext

Get our latest updates on LinkedIn or Twitter.

Want to publish your article on Kontext?

Learn more

More from Kontext

Pandas DataFrame Plot - Line Chart
visibility 529
thumb_up 0
access_time 10 months ago

This article provides examples about plotting line chart using pandas.DataFrame.plot function. The data I'm going to use is the same as the other article  Pandas DataFrame Plot - Bar Chart . I'm also using Jupyter Notebook to plot them. The DataFrame has 9 records: DATE TYPE SALES ...

Create Business Reports using SSRS
visibility 967
thumb_up 0
access_time 7 years ago

Serial: An Introduction to SQL Server Features After the data is loaded into the database, reports can be built using SSRS (SQL Server Reporting Service). The general requirements for in this scenario are: Sales performance in different areas Sales amount for different products ...

Pandas DataFrame Plot - Bar Chart
visibility 2065
thumb_up 0
access_time 10 months ago

Recently, I've been doing some visualization/plot with Pandas DataFrame in Jupyter notebook. In this article I'm going to show you some examples about plotting bar chart (incl. stacked bar chart with series) with Pandas DataFrame. I'm using Jupyter Notebook as IDE/code execution environment.  ...