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How to Connect BigQuery to Power BI

Cody Schneider•

Connecting your Google BigQuery data warehouse to Power BI transforms massive, complex datasets into clear, interactive visualizations. This guide walks you through the straightforward process of linking these two powerful tools, enabling you to build dynamic reports and dashboards. We'll cover the recommended native connector method as well as how to optimize your connection for the best performance.

Why Connect BigQuery to Power BI?

Google BigQuery is a serverless, highly scalable data warehouse designed to run lightning-fast SQL queries on petabytes of data. Power BI is a market-leading business intelligence tool from Microsoft, known for its intuitive interface and powerful visualization capabilities. When you bring them together, you get the best of both worlds:

  • Big Data Analytics: Leverage BigQuery's incredible processing power to analyze massive datasets without worrying about infrastructure management.

  • Interactive Visualizations: Use Power BI's drag-and-drop report builder to create rich, interactive dashboards that make it easy for anyone on your team to understand the data.

  • Real-Time Insights: With the DirectQuery option, your Power BI reports can query BigQuery in real time, ensuring your dashboards always reflect the freshest data available.

  • Unified Reporting: Power BI can connect to hundreds of other data sources, allowing you to combine your BigQuery data with information from marketing, sales, and finance platforms to create a holistic view of your business.

In short, this connection allows you to move from raw data in BigQuery to actionable insights in Power BI with minimal friction.

Before You Begin: Essential Prerequisites

To ensure a smooth connection process, make sure you have the following in place first:

  • A Google Cloud Platform (GCP) Account: You need an active GCP account with billing enabled.

  • BigQuery Project and Data: You must have a project set up in BigQuery with a dataset and at least one table loaded with the data you want to visualize.

  • Correct GCP Permissions: The Google account you use to connect must have at a minimum the BigQuery Data Viewer (roles/bigquery.dataViewer) and BigQuery Job User (roles/bigquery.jobUser) roles assigned in the GCP Identity and Access Management (IAM) settings for the project you want to access.

  • Power BI Desktop: You'll need the latest version of Power BI Desktop installed on your Windows machine. It's a free application available from the Microsoft Store.

Method 1: Using the Native Power BI Connector (Recommended)

The easiest and most reliable way to connect BigQuery to Power BI is by using the built-in connector. It's officially supported and provides the best experience. Here’s how to do it, step-by-step.

Step 1: Open Power BI and Get Data

Launch Power BI Desktop. In the Home tab on the Power BI ribbon, click on Get Data and then select More... from the dropdown menu. This will open the Get Data window, which lists all available data connectors.

Step 2: Select the Google BigQuery Connector

In the Get Data window, use the search bar at the top left and type "BigQuery". You will see an option for Google BigQuery. Select it and click the Connect button.

Step 3: Authenticate Your Google Account

After clicking connect, a dialog box will appear. You will be prompted to sign in with your Google account. Click the Sign in button and enter the credentials for the Google account that has permission to access your BigQuery project.

You may be asked to grant Power BI permission to view your data, you must accept this to proceed. Once authenticated successfully, click Connect to move to the next step.

Step 4: Navigate to Your BigQuery Data

Once you are authenticated, the Power BI Navigator window will appear. This window displays all of the Google Cloud projects your account has access to. From here, you can drill down to find the specific table you want to analyze.

  1. Find your project in the list and click the arrow next to it to expand it.

  2. Expand the dataset that contains your target data.

  3. Select the checkbox next to the table (or tables) you wish to work with.

As you select a table, Power BI will show you a data preview on the right side of the window, so you can confirm you've chosen the right one.

Step 5: Choose Your Data Connectivity Mode

At the bottom of the Navigator window, you’ll see two critical options for how Power BI will interact with your data: Import and DirectQuery. The mode you choose has significant implications for performance, data freshness, and functionality.

Import Mode

In Import mode, a copy of your data from the selected BigQuery table is loaded into the Power BI file (.pbix). This data is compressed and stored within Power BI's high-performance in-memory engine.

  • Pros: Excellent performance because all processing happens locally. You get access to the full capabilities of Power Query and all DAX functions.

  • Cons: The data is only as fresh as the last refresh. Scheduled refreshes are required to pull in new data. There are also data-size limitations, though modern Power BI can handle very large imported datasets.

  • Best For: Datasets under a few gigabytes, situations where data doesn't change every minute, and when you need to perform complex data transformations in the Power Query Editor.

DirectQuery Mode

In DirectQuery mode, Power BI does not store a copy of the data. Instead, it sends live queries directly to BigQuery every time a user interacts with a report (e.g., clicking a slicer or filter). This means your visuals are always based on the most current data.

  • Pros: Ideal for extremely large datasets that won't fit into memory. Your reports will always show data in real-time.

  • Cons: Report performance depends entirely on the speed of BigQuery and your network connection. Certain complex DAX functions and Power Query transformations are not supported.

  • Best For: Real-time dashboards, KPI monitoring, and datasets measured in terabytes or petabytes where importing the data is not feasible.

Step 6: Load or Transform Data

After selecting your table(s) and connectivity mode, you have two final choices:

  • Load: This option will immediately load the data directly into your Power BI data model as-is. This is a quick way to get started if your data is already perfectly clean.

  • Transform Data: This is the recommended option for most cases. Clicking it opens the Power Query Editor. Here, you can clean and shape your data before it is loaded. This is where you can remove unnecessary columns, change data types, split columns, filter rows, and merge data from other tables without affecting the source data in BigQuery. Performing these transformations early streamlines your report and improves performance.

Once your data is shaped to your liking in the Power Query Editor, click Close & Apply in the top-left corner. Your data from BigQuery is now available in Power BI, ready for you to start building reports and visuals!

Using Custom SQL with the Native Connector

What if you don't need an entire table? You can improve performance by only pulling the exact data you need with a custom SQL query. This is handled through the connector's advanced options.

When you first connect to Google BigQuery (Step 2 above), look for the Advanced options dropdown. Clicking it reveals a text box where you can input a SQL statement.

For example, you could write a query to only pull sales data from the last year for specific products:

Using a SQL statement is highly efficient. It pushes the work of filtering, aggregating, and joining data back to BigQuery, which is built to do it at scale, reducing the amount of data Power BI has to handle and dramatically speeding up the report building process.

Troubleshooting Common Connection Issues

If you run into trouble, it's often due to one of these common issues:

  • Permission Errors: If you receive an "Access Denied" or permission-related error, it almost always means the Google account you used to sign in lacks the necessary IAM permissions in GCP. Double-check that you have been granted both BigQuery Data Viewer and BigQuery Job User roles for that project.

  • Authentication Problems: Sometimes, old or incorrect credentials can get cached. In Power BI Desktop, go to File > Options and settings > Data source settings. Find your BigQuery entry, select it, and click Clear Permissions. Then try the connection process again to force a new sign-in.

  • DirectQuery Performance Is Slow: If your visuals are loading slowly in DirectQuery mode, the issue lies with the queries being sent to BigQuery. Simplify your visuals, reduce the number of visuals on a single page, and ensure your underlying data tables in BigQuery are partitioned and clustered for performance.

  • Billing Project is Not Set: BigQuery jobs require a billing project to be associated with them. Power BI's native connector typically handles this, but if you get an error message about billing, ensure your BigQuery project is correctly linked to a billing account in the Google Cloud Console.

Final Thoughts

Connecting Google BigQuery to Power BI allows you to combine an enterprise-grade data warehouse with a leading self-service business intelligence platform. The native connector makes it easy to choose between high-performance data imports or real-time queries, giving you the flexibility to build insightful, data-driven reports no matter the scale of your data.

Learning how to connect individual sources like BigQuery is a great skill, but the real time sink often comes from managing reports across a dozen different marketing and sales platforms. That's why we built Graphed. We connect directly to all your data sources - from Google Analytics and Facebook Ads to Salesforce and Shopify - so you can create real-time, unified dashboards just by describing what you need in plain English. Instead of toggling between complex BI tools and manually stitching data together, you can get the answers you need in seconds, freeing you up to act on insights instead of just gathering them.