How to deal with the GA4 Data API quota limitations in Looker Studio

Recently we have written about some changes to the GA4 Data API quota limits and how these may affect your Google Looker Studio (FKA Data Studio) dashboards, or any other tools that use GA4 Data API to pull in the data. Although the Looker Studio team is actively working on plans to address the issue, we still have no definite ETA and we don’t know if the solution will indeed solve the problem. With the latest release from the Looker team, we are now able to monitor the quota usage. This update together with the previously mentioned optimisation suggestions (and Google’s quotas guide), will hopefully help with reformatting your dashboards to make them usable again.

You can check our suggestions on how to deal with the quota limits in our previous blog and also follow some additional best practices to reduce the amount of data that is queried from GA4.

As mentioned before, we think that the proposed solutions are far from ideal, but we decided to test some of them to see how they compare to one another.

Option 1 – BigQuery

If you already use BigQuery for more complex analyses, know how to use SQL and the cost is not a concern, you could look into either linking your GA4 proprty to BigQuery using the native in-product linking, or setting up data pipelines using one of the ETL third-party tools (e.g. Skyvia, Stitch or Funnel to name only a few).

GA4’s daily and streaming BigQuery exports

The benefit of this approach is that you have event-level raw data that you can not only use for simple dashboards but also for more complex analyses. The setup is easy and you won’t have to worry about things like data retention.

However, the GA4 native exports lack some of the key fields that you might typically use in your reports, such as channel grouping, session-level campaign data or Google Ads data. For users who are not familiar with the nested tables, using these in Looker Studio may be challenging.

To deal with the limitations you can try to re-create the channel attribution (stay tuned – soon we will share our own solution) and connect your Google Ads data using data transfers to enrich your data, but this will require some advanced SQL knowledge.

The other thing that you would have to consider is cost, which will depend on the amount of queried and stored data, and whether you use any complex custom queries. You can of course mitigate the cost by creating smaller summary tables that you then use in your dashboards.

Data pipelines and ETL tools

For our testing, we used Stitch, as we are familiar with this product, however, there are many other ETL tools that you could choose from. The great thing about using Stitch is that you can get the fields from GA4 that are not available with the native BigQuery > GA4 integration, such as channel grouping or Google Ads data. You can also do additional analysis and data transformation in BigQuery before connecting to your dashboard – the data, however, is aggregated so you won’t be able to do more complex analyses as you would with the raw export.

As you would still store your data in BigQuery, you will incur costs relating to storage and querying on top of the fees that you would have to pay for the tool itself. You are also limited in terms of how many fields you can pull in a single pipeline which actually shouldn’t be a big issue, as you would probably want to split the data into smaller tables anyway to avoid high costs when querying. It is also worth mentioning that the GA4 connector is currently in beta and it lacks the ability to filter or use segmentation, this will lead to a greater number of rows than is actually required and a higher cost.

Option 2 – Partner Connectors

If you are not familiar with SQL and are concerned about the costs associated with BigQuery, you can instead look at using one of the partner connectors available directly in Looker Studio. There is some cost associated with using those connectors, but this can be very low depending on your needs and the type of connector you choose. These are mostly easy to set up and you will be able to get the same data as you would using the Google Analytics Looker Studio native connector.

Here is a quick comparison of some of the partner connectors:

Supermetrics

Pros:

• Easy and quick connection and set-up
• Comes with a report template
• Relatively cheap if only using the basic membership
• No sampling

Cons:

• The custom fields option is only available with the “Super” membership at €499
• A bit slow, but this may depend on the size of your property and can be comparable to the default GA4 connector

Windsor.ai

Pros:

• Easy and quick connection and set-up
• Quicker than Supermetrics (at least in the tests we run. Check also this video from JJ Reynolds where they test Supermetrics and other connectors)
• There is a free package available that only has one data connector and user but could be sufficient for some users
• Can create custom fields even with the free version
• The Basic package is the cheapest of all connectors we tested

Cons:

• The free option has a limited number of time periods you can select from in the original data pull. This limitation is lifted when upgrading to the Basic package

Analytics Canvas

Pros:

• You can join other data sources and do transformations within the Analytics Canvas UI

Cons:

• A more complex set-up. Some tutorials might be required. It’s more like an ETL tool than just a simple connector
• There is a limit to how many metrics/dimensions you can choose, with a total of 9 dimensions and custom dimensions and a total of 10 metrics
• Can’t get more than 2 years of data (which should be sufficient for most users)

Adverity

Pros:

• You can apply dimension and metrics filters before connecting to Looker Studio
• You can add “enrichments”, i.e. connect to other data sources, format data, add custom columns/fields
• You can choose from available data streams (tables) or create your own

Cons:

• No pricing information on the site
• Requires a more complex custom set-up within the tool and might need a bit of reading to learn how to use it

Conclusion

Have you tried any of these options? Let us know what worked for you! And if you need help choosing the best solution for your business, get in touch and we can help you decide on the best way forward for you and your business.