You can set up a connection between Klipfolio and your Google BigQuery data to display your Google Storage data on a dashboard.
This article includes:
- Options for visualizing my Google BigQuery data
- Connecting my Google BigQuery data in Klipfolio
- Adding a Google BigQuery pre-built or custom-built data source
- Creating a Google BigQuery custom Klip
- More about Google BigQuery's API and queries
What options do I have for visualizing my Google BigQuery data?
Create data visualizations based on pre-built options or build your own. The choice is yours!
Pre-built by Klipfolio
Take advantage of our pre-built data sources to gather the data most meaningful to you.
- Google BigQuery pre-built data sources: Data sources provide the foundation for Klip building, so we suggest you create a data source as a first step (before building Klips and adding them to your dashboard). Our pre-built data sources focus on the most sought-after Google BigQuery data and are found on the Service Connectors page, available from this link or from your list of data sources. When you select a service connector from this page, the data source for that service is automatically added.
Custom-built by you
Taking the time to learn how to create your own data sources and Klips unlocks your data and enables you to control, manipulate, and transform it, leading you to unprecedented insights into your business.
- Custom data sources: When you create a custom data source, you can either use the pre-built data source as a starting point from which to write your own query or you can write your own queries from the ground up and build a data source from scratch. You create custom-built data sources in the Service Connectors page, accessible from this link or from your list of data sources.
- Custom Klips: Using our Klip Editor, you can construct and manipulate your data, including integrating multiple data sources, to produce unique, custom-built data visualizations. You create custom Klips from either your list of Klips or your Dashboard.
Regardless of whether you choose pre-built, custom-built, or a combination of both, before you can start building Klips and dashboards you need to connect to Google BigQuery data in Klipfolio.
How do I connect to Google BigQuery data in Klipfolio?
When you connect your Google BigQuery data to Klipfolio, you will be prompted to connect your Google BigQuery account by entering your username and password.
Doing so will create a token that enables Klipfolio to securely access your Google BigQuery account. Your token looks like George@Google. You can use and reuse the same token every time you connect to Google BigQuery.
How do I add a Google BigQuery pre-built or custom-built data source?
If you choose to create a custom Google BigQuery data source, you first need to create an SQL query using the Google BigQuery Query Editor. You will copy and paste that query into Klipfolio during the custom data source creation process.
To add a Google BigQuery pre-built or custom-built data source:
- Navigate to the Service Connectors page in Klipfolio and choose Google BigQuery from the list.
- Select a pre-built data source from the left section of the Choose a pre-built or custom connection Page. Alternatively, if you want to create a custom-built data source, click Create a custom Google BigQuery data source.
- If this is your first time connecting to Google:
- Click Connect an account. Enter your Google BigQuery login credentials and click Next. If prompted, click Allow Access to enable Klipfolio to securely access your Google BigQuery data. Click Continue.
- If you have connected with Google BigQuery before:
- Select an existing connection (token) from the drop-down list and click Continue.
- On the Configure your data source page:
- If you selected the List of Projects pre-built data source, click Get data.
- If you selected the Sample Query - Life Expectancy by Country pre-built data source, select a Project from the drop-down menu (or, optionally, manually enter the Project name/ID in the text field). At Request Option, select either BigQuery SQL or Raw data (JSON), depending on the desired output format, and click Get data.
- If you selected Create a custom Google BigQuery data source, select a Project from the drop-down menu (or, optionally, manually enter the Project name/ID in the text field). At Request Option, select BigQuery SQL, paste the SQL query you copied from the Google BigQuery Query Editor into the SQL Query text box. Click Get data. If needed refer to the Google BigQuery API documentation for details.
Note: Be sure to include
#standardSQLas the first line in your query, otherwise you may receive a date format error.
The image below displays a Google BigQuery SQL query and its results in Klipfolio:
- Ensure this is the data you’re looking for and, optionally, select the checkbox to Model your data. Learn more about modelling your data source here.
- Click Continue.
- Name, choose refresh settings, and, optionally, share your data source.
- Click Save.
How do I create a Google BigQuery custom Klip?
You create custom Klips from either your list of Klips or your Dashboard. You choose a data visualization format, for example, a pie chart, gauge, or table, and decide whether you want to use an existing data source or create a new one. In the Klip Editor, there are limitless opportunities to modify, relate, and display your data. See the following related articles for more information and get building!
More about Google BigQuery's API and queries
Klipfolio connects to hundreds of services in the cloud that have a REST API. If you take the time to learn your service’s API to create queries for the data you want, your possibilities are endless. Here’s a quick overview video on APIs and Klipfolio:
If you need assistance using APIs, enlist a data analyst or developer.
Refer to the Google BigQuery API documentation to discover more data requests.
See the reference table below for API related information and sample queries.
|API Documentation||Google BigQuery API documentation|
|OAuth Token Authentication|
|Response Format||CSV or JSON|
Life Expectancy by Country in Raw data (query in JSON format):