How to Connect BigQuery To Power BI?: A Comprehensive 101 Guide
Do you use Google BigQuery to store data and Power BI as a BI tool? Did you invest several hours on the internet to connect these components? If yes, then this blog will answer your queries. This blog takes you through Google BigQuery, Microsoft Power BI, and steps to connect Google BigQuery to Power BI.
Table of Contents
Power BI is a business analytics service by Microsoft. It supports in-built connectors for various sources, including Google BigQuery. We can use this connector to establish the integration. You can either get a copy of your data from BigQuery or get a live connection with your BigQuery data. For this module, you will use a copy of the public data set offered by BigQuery.
Table of Contents:
- What is Google BigQuery?
- What is Microsoft Power BI?
- Prerequisites
- What Are the Two Modes for Loading Data from BigQuery to Power BI?
- How to Connect Google BigQuery to Power BI?
- What Are the Limitations While Connecting BigQuery to Power Bi?
- Why Connect BigQuery to Power BI?
- Conclusion
What is Google BigQuery?
Google BigQuery is a serverless and fully managed warehouse that enables scalable analysis. It is a Saas that supports querying using ANSI SQL. Petabytes of data can be processed by Google BigQuery easily. It consists of the following three parts:
- Storage: Data is stored in tables that can be accessed and analyzed using SQL. It automatically manages all the storage and scaling operations for you.
- Ingestion: You can upload data from your cloud storage, or build an ETL pipeline and import data in multiple formats.
- Querying: ANSI SQL is used for query processing in Google BigQuery.
You can also bypass the steps of storage and ingestion by using BigQuery’s public data sets. The public data sets are third-party data sets that are made public so that anyone can query it.
BigQuery offers flexible and scalable pricing based on storage and querying. Read more about the pricing here.
What is Microsoft Power BI?
Power BI is a business analytics tool introduced by Microsoft. It is one of the most popular BI tools in the market. It is a collection of apps, software services, and connectors that work together to turn your unrelated data into an interactive and coherent dashboard and report. Power BI uses the Data Analysis Expression (DAX) language. DAX consists of various functions and expressions that are used to build formulas to understand data. Power BI is available as Power BI Desktop, Power BI Service, and Power BI Mobile. You can use any of the available tools for analysis.
Key features of Microsoft Power BI
- Customize Visualization: You can create your visualizations using custom visualize SDK.
- Multiple Data Sources: Power BI supports inbuilt connectors for more than 100 sources such as Oracle, Azure, SQL Server, BigQuery, etc. For data sources like MongoDB, you can use the ODBC connector as there is no in-built connector in Power BI.
- Real-Time: Power BI allows you to access your data in real-time. It helps us grab opportunities and solve problems.
- Interactive Models: Everyone in the organization can create interactive reports. To ensure that your audience gets most of your dashboards, you can add custom Q&A buttons.
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Prerequisites
- A Google BigQuery account.
- A dataset in BigQuery for visualizing.
- Power BI Desktop.
- You need to have basic knowledge of BigQuery and BI tools.
What Are the Two Modes for Loading Data from BigQuery to Power BI?
The two modes are:
1) Import Mode
If you’re loading data from BigQuery to Power BI using import mode, then you can manipulate the data according to your requirements. You can change the format, add or remove rows/columns, etc.
2) DirectQuery Mode
If you’re loading data from BigQuery to Power BI using DirectQuery mode, then the data from BigQuery will be synchronized with Power BI but the preview won’t be available.
Now, let’s have a look at the steps involved in connecting BigQuery to Power BI.
How to Connect BigQuery to Power BI?
You can connect your BigQuery data to Power BI using the in-built connector provided by Power BI. Follow these steps to achieve the BigQuery to Power BI integration:
- In Power BI, select “Get Data” in the home ribbon.
- In the Get Data panel, select “Google BigQuery” and proceed by clicking “Connect.”
- Sign in using your Google BigQuery account and select “Connect.”
For Organizational account:
For Service Account:
- In the navigator window, select your data set and click “Load.”
- In the connection settings, you can select either “Import” or “DirectQuery.” Click “Load.”
- Now, your data is loaded in Power BI and is ready to visualize.
What Are the limitations While Connecting BigQuery to Power BI?
The following limitations are encountered while connecting BigQuery to Power BI:
- If you are using a large data set such as a public data set offered by BigQuery, then it will take a lot of time to load it in Power BI.
- Security is an issue as there is no two-factor authentication. You can just sign in and load your data in Power BI.
Why Connect BigQuery to Power BI?
Here are a few reasons for connecting BigQuery to Power BI.
- All your data is available in one go in Power BI. This will save you a major chunk of time from manually loading data from multiple sources into Power BI.
- Easy selection and deselection of source data sets in a click. You don’t need to worry about data siloes anymore.
- You can explore with numerous visualizations in Power BI because you have access to your organization’s Single Source of Truth (SSOT), i.e, BigQuery in this case.
Conclusion
In this blog, you have learned about Google BigQuery, Microsoft Power BI, and the steps to use the inbuilt connector to connect Google BigQuery to Power BI. You have also learned about the limitations that you will encounter while integrating these two platforms. Google BigQuery is used by various organizations to store their data. But if your data is not on a single platform, and you want to integrate it before analysis, then you can try Hevo.
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Share your experience of connecting BigQuery to Power BI in the comment section below.
