Airtable offers a vast reporting capability, available both via its apps and through custom scripts. In the blink of an eye, you can build rich charts, customize them, and enrich them with data from different Airtable bases. And if that’s not enough, you can always connect Airtable to Data Studio to get more from your data.
In this article, we’ll explain a simple way to integrate both tools and sync data between them automatically, with no coding or scripting required.
Why connect Airtable to Google Data Studio?
As we talked about in our article on Airtable reports, it’s very easy to draw simple and more advanced charts using the built-in Airtable functionalities. This feature is available for users on the Pro and Enterprise plans and will do the job just right for many.
If you’re not on either of these plans or simply need more from your data, Data Studio is made for the occasion. Its dashboards are simple to put together and will provide an excellent representation of your Airtable data. What’s more, Data Studio is free to use.
Airtable dashboard integration with Data Studio also has another benefit. By pulling your Airtable bases into Data Studio, you can blend them with any other data at your disposal. Google’s product makes it very easy to combine different datasets on charts and demonstrate the performance from many different angles.
Add this to the ease of connecting both tools, and it’s no wonder that Data Studio is such a popular choice among Airtable users.
Reports from Airtable data in Google Data Studio
There’s no official integration between Airtable and Data Studio. Because of that, the process needs to be broken down into two steps:
- Exporting data using an intermediary app, for example, a Google Sheets file
- Pulling that data into Google Data Studio
Data Studio features a number of native connectors for other Google products. For that reason, it’s by far the easiest to use Google Sheets as an intermediary. Alternatively, for larger data sets, you may opt for exporting Airtable to BigQuery instead. We’ll explain both approaches.
This whole process may seem complex and time-consuming, but it really doesn’t have to be. With Coupler.io’s integrations, it requires no coding, is quick to set up, and the data flow will happen automatically, and as often as you would like it to. Let’s see how it works.
Option 1 – Export Airtable data to Google Sheets
First things first, create a Coupler.io account if you don’t have one yet. Then, click the Add new importer button.
From the list of available integrations, select Airtable. Optionally – name the importer at this point.
With Coupler.io, you can import a specific Airtable dashboard view, one at a time – a limitation of the Airtable API.
Don’t feel limited by this, though, if you were planning to import multiple bases or an entire Airtable workspace. Once you’ve configured an importer, you can duplicate it and amend a view URL in just a few clicks.
To get a Shared URL of a particular view, load the view in Airtable that you want to import. Click on Share view in the menu and copy the URL you’ll see below. Paste it into the respective field in Coupler.io.
If your view is password-protected, type in the password below. Otherwise, click Continue to jump to the Destination settings.
Select Google Sheets as a destination. If you prefer BigQuery, jump to the next chapter.
Add your Google account and give the necessary permissions to Coupler.io when asked. Then, select the spreadsheet and the sheet where you want to import the data. If the sheet doesn’t exist, just type in its name, and it will be created.
Leave the optional settings as they are and jump to the final stage. Here, select the schedule for the data refresh.
When you’re happy with all settings, click the Save and run button. After the import has finished, click View results to see your Airtable data in a Google Sheet. Here’s an example:
Option 2 – Export Airtable data to BigQuery
For very large bases, BigQuery is often a better solution.
The Source part of the importer setup is identical to what we described for Google Sheets. You select Airtable as a source, add a Shared URL, and jump to the Destination settings.
Here, of course, you ought to pick BigQuery. Then, click Continue.
A new window will open but ignore it for now. Jump to your BigQuery console. From the left-most menu, select IAM & Admin and then Service Accounts. Click Create Service Account on top.
Name the account and press Create and Continue. In the next stage, add the following roles:
- BigQuery Data Editor
- BigQuery Job User
Press Continue and then Done.
Now, find the service account you just created. Click on the three dots to its right and select Manage keys. Select Add Key, then Create new key. Stick to the default JSON type and press Create.
A JSON file will upload. Select it on the previously opened Coupler.io connection page and press Save.
To finalize the connection, type in the dataset and table to import the data to. Note that a table doesn’t need to exist before – typing a new name will create a table when the importer is launched.
As the last step, set up a schedule as we did before and press Save and run. Here’s a preview of our Airtable to BigQuery import:
Add the Airtable data to Google Data Studio
Now the second stage of the setup. You already have the Airtable data either in Google Sheets or BigQuery, and it will be refreshed according to the schedule you’ve chosen. Let’s connect the source to Data Studio, then.
In Data Studio, click Create and then Data source.
Then, select either the Google Sheets or BigQuery connector.
In the next stage, find the sheet or table where you jump imported the Airtable data. Select it, and press Continue.
Next, you’ll see the list of imported dimensions and metrics. You can adjust their type if you’d like, change the refresh rate (by default, it’s every 15 minutes), or adjust some other settings. Then, press Create Report.
When asked, choose Add to Report.
If you have data in multiple sheets or tables, you can repeat the process multiple times, adding them one by one.
And now, it’s time to start building your first Airtable report. If you’re new to Data Studio, be sure to check our Google Data Studio tutorial.
Airtable dashboard integration – Data Studio vs. Tableau vs. Power BI
Google Data Studio is just one of the many data visualization tools that can be connected to Airtable. It is frequently compared to two others: Tableau and Microsoft Power BI.
Tableau is considered one of the most powerful DataViz tools on the market. It can efficiently crunch huge data sets and offers more advanced visualization techniques than its Google’s counterpart. It offers a cloud-based application as well as desktop software.
Tableau also differs from Data Studio when it comes to data sources. Data Studio offers native connectors only with other Google products, integrating with others via third-party connectors. Tableau works with several Google products – for example, BigQuery or Google Analytics – but it’s quite limited.
However, its true power comes from hundreds of native integrations with virtually any notable database and storage system. As such, it’s more targeted at enterprises while Data Studio is more suitable for startups and small businesses.
See how both tools compare in our Tableau vs Data Studio comparison.
Tableau doesn’t have direct Airtable integration but you can take an identical approach as was the case for Data Studio. First, export the data into an intermediary app using Coupler.io. Then, plug that data into Tableau using the available connections.
Microsoft Power BI is another popular DataViz tool. It offers more visualization techniques than Data Studio but one could also say it’s a bit less intuitive than Google’s products – although that very much depends on who you ask. Frequent users of Excel, for example, will probably have an easy time getting started with Power BI.
Power BI comes with a cloud-based service as well as Power BI Desktop that features even more functionalities.
Power BI is most suitable for frequent users of other Microsoft products. It features native integration with MS Office, Azure, Sharepoint, and others. It also works with different databases as well as some marketing and sales tools, such as Salesforce or Google Analytics.
When it comes to Airtable integration, it’s also best to take it the “Microsoft way”. First, schedule an Airtable export to Excel using Coupler.io. Then, follow our tutorial to connect Excel to Power BI.
Airtable – Data Studio integration recap
Airtable makes it very easy to import data into the platform. One can use its Google Sheets and Excel importers or simply drop a CSV file with virtually any dataset they wish to turn into a base.
Things get a bit more tricky when it comes to exporting data from Airtable. Sure, you can always pack the contents of a base into a CSV file and upload it anywhere else – if you rely on regular data exports, though, this approach won’t work.
Luckily, there’s an Airtable API and various applications that simplify the process if you don’t want to code. The Airtable to Data Studio flow that we described takes maybe 5-10 minutes to set up and will keep your data fresh for as long as you need it.Back to Blog