Google Analytics 4

Google Analytics 4 (GA4) is a web analytics platform that provides businesses with advanced tracking and measurement capabilities, focusing on a user-centric data model that enables tracking of user interactions across multiple devices and platforms, allowing for a more comprehensive understanding of customer behavior and journey. GA4 also incorporates machine learning features to provide actionable insights and predictions for businesses to optimize their marketing strategies.

For all available GA4 metrics and dimensions, please see Google's official documentation.

How to Create a New Google Analytics 4 Data Source

Connector Overview

Metrics and Dimensions Combinations

Not all GA4 metrics and dimensions combinations will work, even though they might be available to select. To check which metrics and dimensions will work together, please see the official GA4 website.

Multi-account extraction

The Dataddo Google Analytics 4 connector supports automatic extraction from all available Google Analytics 4 accounts. This feature is particularly useful when you have multiple accounts and need to automatically extract the same metrics and dimensions across them (e.g. agencies).

Limitations

Historical Data

The GA4 API allows you to extract and load up to 1 year of historical data.

Fresh Data Unavailable

Google takes between 24 – 72 hours to process data which is why it’s possible to get data only from 3 days ago or older.

For Google Analytics, YouTube, or Google My Business, change your date range expression to 3d3 or older.
For Google Search Console, change your date range expression to 4d4 or older.

You may get fresh data from Google Analytics 4 but keep in mind that it might not be the finalized version.

What If I Need Fresh Data?
We can retrieve your data from the last 3 days from Google Analytics 4 but keep in mind that this data will most likely change.

See how you can extract fresh data to your data-warehouse or a dashboarding tool.

Get Fresh GA4 Data to a Data Warehouse

First, make sure that deduplication is possible in your case. Using the Upsert snapshot keeping policy in combination with the 3d1 or 3d0 date range expression, you can extract last 3 days worth of data every day.

1. Click on the three dots next to your source and choose Edit.
2. In the Snapshotting tab, select Upsert.
3. In the Advanced tab, change the date range to 3d1 or 3d0.
4. Save your configuration.
   

Upsert (limited to 4 unique columns) will be directly replacing the data with the most recent values. In case Upsert is not possible (for example, you need more than 4 unique columns), you can deduplicate in your own storage using dataddo_extraction_timestamp.

Get Fresh GA4 Data to a Dashboarding Tool

Create two sources, one for historical data, and the second for fresh data.

The source with historical data should add data from 4 days ago to your table every day. We can achieve that with the following configuration:

• Date range expression: 4d4 or older
• Snapshot keeping policy: Append
  

The source with fresh data should be a running window of the last three days. As a result, it will always contain the last 3 days' fresh data without duplicates. We can achieve that with the following configuration:

• Date range expression: 3d1 or 3d0
• Snapshot keeping policy: Replace
  

As both of these sources have the same schema, they can be added into the same flow, not affecting your billing.

Troubleshooting

Duplicates

Too many dimensions when configuring the source were selected, please replace with a source with less dimensions or make sure these dimensions can work together.

Example: We want to get the total daily number of sessions.

Original table

Pagepath dimension (what pages are visited by every user) is added:

Total number of sessions: 3

Where is the problem?

One user visited both Page 1 and Page 2 during the same session.
The same session appeared twice in combination with each page, aka it resulted in a duplicate.

Missing data

Missing data occurs when the total values was broken down into subcategories and the sum of the two subcategories differs. When working with your data, you need to keep this difference in mind.

This error is caused by Google being unable to retrieve the subcategory information on all of its users and this value is left blank. These blank values are then disregarded instead of being put into their own category.

This error happens most often with Gender or Country dimensions so keep the possibility of this error in mind when working with them.

Example: We want to get how many men and how many women visited my website last week.

Original table

Gender dimension is added:

What about the rest 100 users?

This error occured because Google was not able to retrieve information on 100 users’ gender and the undetermined values were not put into a separate row.

No Data Causing Source to Break

If you have no data in your source, it may lead to a broken source and further issues with synchronization. When checking the Allow Empty box in the source's settings, you will be able to send "empty" data.