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: https://bigquery.cloud.google.com/table/bigquery-public-data:world_bank_intl_debt.international_debt?pli=1&tab=details
Public data sets are paid by Google for storage but you need to pay for querying it.
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.
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:
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.