This package connects to an exported GA4 dataset and provides useful transformations as well as report-ready dimensional models that can be used to build reports or blend GA4 data with exported GA3 data.

Features include:

• Flattened models to access common events and event parameters such as page_view, session_start, and purchase
• Conversion of sharded event tables into a single partitioned table
• Incremental loading of GA4 data into your staging tables
• Session and User dimensional models
• Easy access to query parameters such as GCLID and UTM params
• Support for custom event parameters & custom user properties

Add the following to your packages.yml file:

packages:
  - package: Velir/ga4
    version: [">=0.1.0", "<0.2.0"]

Add the following to your packages.yml file:

packages:
  - git: "https://github.com/Velir/dbt-ga4.git"
    revision: 0.1.3

1. Clone this repository to a folder in the same parent directory as your DBT project
2. Update your project's packages.yml to include a reference to this package:

packages:
  - local: ../dbt-ga4

This package assumes that you have an existing DBT project with a BigQuery profile and a BigQuery GCP instance available with GA4 event data loaded. Source data is located using the following variables which must be set in your dbt_project.yml file.

vars:
    ga4:
        project: "your_gcp_project"
        dataset: "your_ga4_dataset"
        start_date: "YYYYMMDD" # Earliest date to load
        include_intraday_events: true # false|true depending on whether an intraday event table exists

If you don't have any GA4 data of your own, you can connect to Google's public data set with the following settings:

vars:
    project: "bigquery-public-data"
    dataset: "ga4_obfuscated_sample_ecommerce"
    start_date: "20210120"

More info about the GA4 obfuscated dataset here: https://support.google.com/analytics/answer/10937659?hl=en#zippy=%2Cin-this-article

One important feature of GA4 is that you can add custom parameters to any event. These custom parameters will be picked up by this package if they are defined as variables within your dbt_project.yml file using the following syntax:

[event name]_custom_parameters
  - name: "[name of custom parameter]"
    value_type: "[string_value|int_value|float_value|double_value]"

For example:

vars:
  ga4:
    page_view_custom_parameters:
          - name: "clean_event"
            value_type: "string_value"
          - name: "country_code"
            value_type: "int_value"

User-scoped event properties can be assigned using the following variable configuration in your dbt_project.yml. The dim_ga4__users dimension table will be updated to include the last value seen for each user.

user_properties:
  - event_parameter: "[your event parameter]"
    user_propertyname: "[a unique name for the user property]"
    value_type: "[one of string_value|int_value|float_value|double_value]"

For example:

vars:
  ga4:
      user_properties:
        - event_parameter: "page_location"
          user_property_name: "most_recent_page_location"  
          value_type: "string_value"
        - event_parameter: "another_event_param"
          user_property_name: "most_recent_param"  
          value_type: "string_value"

This package assumes that BigQuery is the source of your GA4 data. Full instructions for connecting DBT to BigQuery are here: https://docs.getdbt.com/reference/warehouse-profiles/bigquery-profile

The easiest option is using OAuth with your Google Account. Summarized instructions are as follows:

1. Download and initialize gcloud SDK with your Google Account (https://cloud.google.com/sdk/docs/install)
2. Run the following command to provide default application OAuth access to BigQuery:

gcloud auth application-default login --scopes=https://www.googleapis.com/auth/bigquery,https://www.googleapis.com/auth/iam.test

This package uses pytest as a method of unit testing individual models. More details can be found in the integration_tests/README.md folder.