Google Analytics 4 to BigQuery: Fast, Secure, Scalable Connection

By My Linh Phung | 6 min read

In today’s world, an online presence is a prerequisite to the success of most businesses. But actually staying visible online has a lot to do with optimization, which, in turn, is closely connected with data-driven decision-making. This is why platforms like Google Analytics 4 (GA4) have gained so much importance. However, the amount of the data they generate eventually becomes so great that it’s impossible to manage without a data warehouse such as Google BigQuery.

To connect your GA4 data to Google BigQuery, you can either use the native integration provided by Google, or an external method such as a third-party tool like Dataddo. But why exactly should you even consider an external tool for this? Unfortunately, even though the native connector might seem to be the most convenient way, you will inevitably encounter a number of limitations that could become much more time-consuming and costly than you thought.

In this article, we will outline the main benefits and drawbacks of each method of connecting Google Analytics 4 to Google BigQuery so that you can choose the one most suitable for your needs. Let’s get started!

BigQuery Export Integration: Good Enough for Starters

Since Google already provides a guide on linking GA4 to BigQuery, we will spare you the scrolling and head straight to the positives and negatives of this method.

The biggest benefit is very straightforward: through this connection, you get access to raw GA4 data in your database as soon as it’s available. What’s the catch? There is more than one.

  1. The export is limited to 1 million events per day. As events are every user interaction on your website or app, make sure you don’t expect more than 1 million events on a regular basis. Otherwise, the export will be paused unexpectedly.
  2. You can avoid the 1-million-event limit by streaming your data. Although there are no limits for data streaming, proceed with caution as you will be charged per exported gigabyte.
  3. Data is quite literally raw so the necessary basic data transformations will have to be done in BigQuery, which is going to cost extra.
  4. Unless you use only Google Analytics 4, you will need to combine the extracted data with the data you get from your other platforms. Once again, you will need basic transformations to harmonize data types and formats, which could take up quite a lot of your time and lead to unexpected expenses.
  5. There will be a time delay ranging from 24 to 48 hours, as Google Analytics 4 takes time to process the data before exporting it. If you need to work with fresh data, this will be a great setback.

Does this mean that you shouldn’t use the native integration? This fully depends on your business needs. If you don’t have to worry about event limits and combining data from various sources (in other words, if you don’t use platforms other than GA4), this method might be the most suitable for you.

It’s also worth noting that if you’re at the stage where this method is the most suitable for you, you might not even need a storage like BigQuery in the first place. We’d recommend either connecting your data directly to a dashboard (free with Dataddo!) or trying it out first in the BigQuery Sandbox environment.

External Data Extraction: When Scalability Is Essential

At some point, your business will outgrow the native integration and you will need to look for alternative solutions. There are two main ways to do this: write your own script or use an external solution such as Dataddo.

External Solution Benefits

The main advantage of external data extraction over a native connection is that you can perform transformations on your data before it hits the destination. This is undoubtedly going to save you a lot of time and money, especially if you use Google BigQuery, which charges per query.

Before sending your data to BQ, an external solution will allow you to:

  • Harmonize data, which means that e.g. the dates in the tables of all of your sources will be in the same format.
  • Blend data from different tables and even sources together.
  • Join data from tables with the same schema.
With the native integration, all these transformations would need to be done for every table in BigQuery, which requires extra SQL work and thus extra money.

In-House Solution: For the Experienced

If you are experienced with creating and writing your own data pipelines, this article is most likely not for you. However, in case you are still hesitating about which integration to choose, here are the main positives and negatives of this approach.

An in-house solution can be great since you don’t need to rely on third-party tools while also evading the limitations of the native GA4-BigQuery connection. The biggest advantage, without a doubt, is the fact that the solution will be tailor-made for your organization’s needs. Nonetheless, having your own team of dedicated data engineers can be a double-sided blade. On one hand, there are the previously mentioned positives. On the other hand, this means extra time and manpower, and thus expenses.

If the integrations that you need are very specific and not widely used, in-house ETL is the best choice. However, if your connections are quite common and well-known for APIs that change more often than desired, this method could become unnecessarily costly. 

What Can Dataddo Offer?

Data extraction and maintenance of data pipelines is very time- and energy-consuming. By outsourcing these functions, you will be able to focus on the core of your job: data analysis.

Apart from the above-mentioned transformations, Dataddo offers the following benefits:

  • It saves time - Set up your data pipeline in just 3 steps (create your source, create your destination, and connect them in a flow).
  • It saves energy - No maintenance is required from your side. Dataddo’s Solutions Team will take care of all of your pipelines for you, and make sure they are secure and reliable.
  • Delivers data in near real time - One of the most distinct advantages of using Dataddo is the possibility to retrieve fresh GA4 data. In this way, you can avoid the 24-48h delay which occurs with other methods.


In the end, which approach is the best? The native integration, your own solution, or Dataddo?

If you need only one direct connection between GA4 and BigQuery and expect a relatively small amount of data, then the native integration will be most suitable for you. Just make sure to pay attention to the number of events and/or how much data you will be streaming to avoid unexpected interruptions.

External arrangement of data extraction should be considered once:

  1. You need to use numerous platforms for social media, HR, e-commerce, etc.,
  2. The data you generate starts to reach head-spinning volumes, and/or
  3. Considerable data transformations become a prerequisite for your data analysis.

Writing your own script is feasible only if you have experience building and maintaining integrations. Although the solution will be custom-built for your organization, chances are you will soon need a dedicated data engineer to take care of your data pipelines for you. Data pipelines can break easily due to e.g. constantly changing APIs or wildly fluctuating data volumes, so you won’t have much time to focus on data analysis itself, or it will be extra costs.

If you aren’t that familiar with coding, or if the core of your job is to actually work with the data, it will be better to outsource the data extraction part. Third-party tools such as Dataddo will make sure that everything in the background is running seamlessly and reliably so that you can focus on your main tasks.

So, which is the best? As hinted throughout this article, that will fully depend on your data needs. We attempted to outline the main benefits and drawbacks of each method. Hopefully, this will help you when deciding!

Category: Tools, tips-tricks