This dbt 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.
Features include:
- Flattened models to access common events and event parameters such as
page_view,session_start, andpurchase - Conversion of sharded event tables into a single partitioned table
- Incremental loading of GA4 data into your staging tables
- Page, session and user dimensional models with conversion counts
- Last non-direct session attribution
- Simple methods for accessing query parameters (like UTM params) or filtering query parameters (like click IDs)
- Support for custom event parameters & user properties
- Mapping from source/medium to default channel grouping
| model | description |
|---|---|
| stg_ga4__events | Contains cleaned event data that is enhanced with useful event and session keys. |
| stg_ga4__event_* | 1 model per event (ex: page_view, purchase) which flattens event parameters specific to that event |
| stg_ga4__event_items | Contains item data associated with e-commerce events (Purchase, add to cart, etc) |
| stg_ga4__event_to_query_string_params | Mapping between each event and any query parameters & values that were contained in the event's page_location field |
| stg_ga4__users | User ID table built from the GA4 User export table. Flattens user properties and audiences using the user_properties and audiences variables in your dbt_project.yml file. Disabled by default. |
| stg_ga4__client_keys | Clint key table built from the GA4 User export pseudonymous users table. Flattens user properties and audiences using the user_properties and audiences variables in your dbt_project.yml file. Disabled by default. |
| stg_ga4__user_properties | Finds the most recent occurance of specified user_properties for each user |
| stg_ga4__derived_user_properties | Finds the most recent occurance of specific event_params value and assigns them to a client_key. Derived user properties are specified as variables (see documentation below) |
| stg_ga4__derived_session_properties | Finds the most recent occurance of specific event_params or user_properties value and assigns them to a session's session_key. Derived session properties are specified as variables (see documentation below) |
| stg_ga4__session_conversions_daily | Produces daily counts of conversions per session. The list of conversion events to include is configurable (see documentation below) |
| stg_ga4__sessions_traffic_sources | Finds the first source, medium, campaign, content, paid search term (from UTM tracking), and default channel grouping for each session. |
| stg_ga4__sessions_traffic_sources_daily | Same data as stg_ga4__sessions_traffic_sources, but partitioned by day to allow for efficient loading and querying of data. |
| stg_ga4__sessions_traffic_sources_last_non_direct_daily | Finds the last non-direct source attributed to each session within a 30-day lookback window. Assumes each session is contained within a day. |
| dim_ga4__client_keys | Dimension table for user devices as indicated by client_keys. Contains attributes such as first and last page viewed. |
| dim_ga4__sessions | Dimension table for sessions which contains useful attributes such as geography, device information, and acquisition data. Can be expensive to run on large installs (see dim_ga4__sessions_daily) |
| dim_ga4__sessions_daily | Query-optimized session dimension table that is incremental and partitioned on date. Assumes that each partition is contained within a single day |
| fct_ga4__pages | Fact table for pages which aggregates common page metrics by date, stream_id and page_location. |
| fct_ga4__sessions_daily | Fact table for session metrics, partitioned by date. A single session may span multiple rows given that sessions can span multiple days. |
| fct_ga4__sessions | Fact table that aggregates session metrics across days. This table is not partitioned, so be mindful of performance/cost when querying. |
| seed file | description |
|---|---|
| ga4_source_categories.csv | Google's mapping between source and source_category. Downloaded from https://support.google.com/analytics/answer/9756891?hl=en |
Be sure to run dbt seed before you run dbt run.
To pull the latest stable release along with minor updates, add the following to your packages.yml file:
packages:
- package: Velir/ga4
version: [">=6.1.0", "<6.2.0"]
To install the latest code (may be unstable), add the following to your packages.yml file:
packages:
- git: "https://github.com/Velir/dbt-ga4.git"
- Clone this repository to a folder in the same parent directory as your DBT project
- Update your project's
packages.ymlto 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 defined using the project and dataset variables below. The static_incremental_days variable defines how many days' worth of data to reprocess during incremental runs.
vars:
ga4:
source_project: "my_source_gcp_project" # Project that contains raw GA4 data
property_ids: [11111111] # Array of properties to process
start_date: "YYYYMMDD" # Earliest date to load
static_incremental_days: 3 # Number of days to scan and reprocess on each run
When processing multiple properties at a time, the required variables change slightly. See Multi-Property Support section for details on configuring multiple GA4 properties as a source.
vars:
ga4:
source_project: "my_source_gcp_project" # Project that contains raw GA4 data
combined_dataset: "my_combined_data" # Dataset where multi-property data is cloned
property_ids: [11111111,2222222] # Array of properties to process
start_date: "YYYYMMDD" # Earliest date to load
static_incremental_days: 3 # Number of days to scan and reprocess on each run
Setting query_parameter_exclusions will remove query string parameters from the page_location and page_referrer fields for all downstream processing. Original parameters are captured in the original_page_location and original_page_referrer fields. Ex:
vars:
ga4:
query_parameter_exclusions: ["gclid","fbclid","_ga"]
You can remove all query parameters by setting query_parameter_exclusions to *all*.
vars:
ga4:
query_parameter_exclusions: ["*all*"]
By default, parameters are removed from URL fragments (elements after the hash # symbol). If you wish to exclude query parameters but keep URL fragments with the same key, you will need to override the remove_query_parameters macro.
Setting query_parameter_extraction will extract query string parameters from the page_location field into new columns. This can be used to extract advertising click IDs into columns that can be joined with advertising data sets. Ex:
vars:
ga4:
query_parameter_extraction: ["gclid","fbclid","keyword"]
Within GA4, 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"
You can optionally rename the output column:
vars:
ga4:
page_view_custom_parameters:
- name: "country_code"
value_type: "int_value"
rename_to: "country"
If there are custom parameters you need on all events, you can define defaults using default_custom_parameters, for example:
vars:
ga4:
default_custom_parameters:
- name: "country_code"
value_type: "int_value"
Derived session properties are similar to derived user properties, but on a per-session basis, for properties that change slowly over time. This provides additional flexibility in allowing users to turn any event parameter into a session property.
Derived Session Properties are included in the dim_ga4__sessions and dim_ga4__sessions_daily models and contain the latest event parameter or user property value per session.
derived_session_properties:
- event_parameter: "[your event parameter]"
session_property_name: "[a unique name for the derived session property]"
value_type: "[string_value|int_value|float_value|double_value]"
- user_property: "[your user property key]"
session_property_name: "[a unique name for the derived session property]"
value_type: "[string_value|int_value|float_value|double_value]"
For example:
vars:
ga4:
derived_session_properties:
- event_parameter: "page_location"
session_property_name: "most_recent_page_location"
value_type: "string_value"
- event_parameter: "another_event_param"
session_property_name: "most_recent_param"
value_type: "string_value"
- user_property: "first_open_time"
session_property_name: "first_open_time"
value_type: "int_value"
See the README file at /dbt_packages/models/staging/recommended_events for instructions on enabling Google's recommended events.
Specific event names can be specified as conversions by setting the conversion_events variable in your dbt_project.yml file. These events will be counted against each session and included in the fct_sessions.sql dimensional model. Ex:
vars:
ga4:
conversion_events: ['purchase','download']
The stg_ga4__sessions_traffic_sources_last_non_direct_daily model provides last non-direct session attribution within a configurable lookback window. The default is 30 days, but this can be overridden with the session_attribution_lookback_window_days variable.
vars:
ga4:
session_attribution_lookback_window_days: 90
Custom events can be generated in your project using the create_custom_event macro. Simply create a new model in your project and enter the following:
{{ ga4.create_custom_event('my_custom_event') }}
Note, however, that any event-specific custom parameters or default custom parameters must be defined in the global variable space as shown below:
vars:
default_custom_parameters:
- name: "some_parameter"
value_type: "string_value"
my_custom_event_custom_parameters:
- name: "some_other_parameter"
value_type: "string_value"
This package contains two sets of user tables: an original set of user tables implemented from the inception of this package and a new set of user tables designed to use the GA4 BigQuery user export tables that were released after this package was first launched.
The original user tables build one-row-per-user tables and include data like first and last device, first and last geo, user properties, and derived user properties. They need to process all-time data to build these tables. Large sites might want to consider disabling these tables to save costs.
The newer user tables leverage the GA4 user export setting. They are partitioned tables so they are more appropriate for high-traffic sites. They lose the first and last columns and derived user properties, but include user properties, audiences, user LTV, and predictive data.
The GA4 user export tables do not currently support multi-site. There is a multi-site branch that needs testing. If you have a multi-site implementation and wish to use the GA4 user export tables, then please install the user branch in your development environment, configure the various user-specific settings, run dbt, and report any issues or successes on this draft PR. Reach out on the draft PR if you need help with any of this.
The user_properties fields in the events_* and events_intraday_* tables, and the users_* and pseudonymous_users_* tables are in different formats. No settings are shared between the two sets of user tables.
User properties are provided by GA4 in the user_properties repeated field at the event-level in the events_* and events_intraday_* tables. The most recent user property for each user will be extracted and included in the dim_ga4__users model by configuring the user_properties variable in your project as follows:
vars:
ga4:
user_properties:
- user_property_name: "membership_level"
value_type: "int_value"
- user_property_name: "account_status"
value_type: "string_value"
Derived user properties are different from "User Properties" in that they are derived from event parameters. This provides additional flexibility in allowing users to turn any event parameter into a user property.
Derived User Properties are included in the dim_ga4__users model and contain the latest event parameter value per user.
derived_user_properties:
- event_parameter: "[your event parameter]"
user_property_name: "[a unique name for the derived user property]"
value_type: "[string_value|int_value|float_value|double_value]"
For example:
vars:
ga4:
derived_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"
The GA4 user export models are disabled by default.
Enable them by adding the following model configs:
models:
ga4:
staging:
base:
base_ga4__pseudonymous_users:
+enabled: true
base_ga4__users:
+enabled: true
stg_ga4__client_keys:
+enabled: true
stg_ga4__users:
+enabled: true
The GA4 User Export includes a user properties repeated record that stores the user property details. User properties are enabled by adding a list of user property names that match values in the user_properties.value.user_property_name fields of your pseudonymous_users_ and users__ tables as shown below.
vars:
ga4:
user_export_user_properties: ['All Users', 'Purchasers']
Unlike the event_params and user_properties event-level fields, the user-level user properties are keyed off of user_properties.value.user_property_name rather than user_properties.key. Tshe user_properties.key in the user tables is the slot that GA4 uses, slot_01 for example, rather than the name. As a result, user_properties.value.user_property_name in the user tables should be the same as user_properties.key in the event tables.
The GA4 User Export includes an Audiences repeated record that stores the audience membership details. Audiences are enabled by adding a list of audience names that match values in the audiences.name fields of your psuedonymous_users_ and users__ tables as shown below.
vars:
ga4:
audiences: ['Purchases', 'All Users']
This example will add the following columns to the relevant dbt-GA4 models:
- purchases_id
- purchases_name
- purchases_membership_start_timestamp_micros
- purchases_membership_expiry_timestamp_micros
- purchases_npa
- all_users_id
- all_users_name
- all_users_membership_start_timestamp_micros
- all_users_membership_expiry_timestamp_micros
- all_users_npa
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:
- Download and initialize gcloud SDK with your Google Account (https://cloud.google.com/sdk/docs/install)
- 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 unit_tests/README.md folder.
By default, this package maps traffic sources to channel groupings using the macros/default_channel_grouping.sql macro. This macro closely adheres to Google's recommended channel groupings documented here: https://support.google.com/analytics/answer/9756891?hl=en .
Package users can override this macro and implement their own channel groupings by following these steps:
- Create a macro in your project named
default__default_channel_groupingthat accepts the same 3 arguments: source, medium, source_category - Implement your custom logic within that macro. It may be easiest to first copy the code from the package macro and modify from there.
Overriding the package's default channel mapping makes use of dbt's dispatch override capability documented here: https://docs.getdbt.com/reference/dbt-jinja-functions/dispatch#overriding-package-macros
Multiple GA4 properties are supported by listing out the project IDs in the property_ids variable. In this scenario, the static_incremental_days variable is required and the combined_dataset variable will define the dataset (in your profile's target project) where source data will be copied.
vars:
ga4:
property_ids: [11111111, 22222222, 33333333]
static_incremental_days: 3
combined_dataset: "my_combined_dataset"
With these variables set, the combine_property_data macro will run as a pre-hook to base_ga4_events and clone shards to the target dataset. The number of days' worth of data to clone during incremental runs will be based on the static_incremental_days variable.
Some user and session models in this package are designed to have one entry per user or session. This offers maximum data accuracy but it means that these models can not be partitioned which can get expensive.
High-traffic sites may wish to disable these models using the following settings in your dbt_project.yml file. Note that disabling these models will disable some features, like derived session and user properties.
models:
ga4:
staging:
stg_ga4__client_key_first_last_events:
+enabled: false
stg_ga4__derived_session_properties:
+enabled: false
stg_ga4__derived_user_properties:
+enabled: false
stg_ga4__client_key_first_last_pageviews:
+enabled: false
marts:
core:
dim_ga4__sessions:
+enabled: false
fct_ga4__pages:
+enabled: false
fct_ga4__user_ids:
+enabled: false
dim_ga4__client_keys:
+enabled: false
This package attempts to adhere to the Brooklyn Data style guide found here. This work is in-progress.