GA4 Export dbt Package

This dbt package transforms data from Fivetran's GA4 Export connector into analytics-ready tables.

Resources

• Number of materialized models¹: 7
• Connector documentation
  • GA4 Export connector documentation
  • GA4 Export ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog
• dbt Core™ supported versions
  • >=1.3.0, <3.0.0

What does this dbt package do?

This package enables you to produce modeled tables that leverage GA4 Export data and replicate common GA4 reports. It creates enriched models with metrics focused on traffic acquisition, user acquisition, events, conversions, and session analysis.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_ga4_export

Final output tables

By default, this package materializes the following final tables:

Table	Description
ga4_export__traffic_acquisition_session_source_medium_report	Analyzes session-level traffic acquisition to understand which sources and mediums drive website sessions, user engagement, and revenue. Corresponds to the traffic_acquisition_session_source_medium_report GA4 report. Example Analytics Questions: Which traffic sources and mediums generate the most sessions and revenue? How do engagement metrics vary across different acquisition channels? What is the average session duration and event count by source/medium combination?
ga4_export__user_acquisition_first_user_source_medium_report	Tracks first-touch attribution to identify where new users originally come from and measure user acquisition effectiveness by source and medium. Corresponds to the user_acquisition_first_user_source_medium_report GA4 report. Example Analytics Questions: Which first-touch channels are most effective at acquiring new users? How do first user sources compare in terms of session quality and revenue generation? What is the total number of new users acquired by each source/medium combination?
ga4_export__events_report	Summarizes event activity across your digital properties to analyze user interactions, engagement patterns, and revenue generated from specific events. Corresponds to the events_report GA4 report. Example Analytics Questions: Which events are triggered most frequently and generate the most revenue? How do average events per user vary across different event types? What is the average revenue per event by event type?
ga4_export__conversions_report	Tracks key events (conversions) to measure goal completions, user actions, and total revenue to understand conversion behavior and performance. Corresponds to the conversions_report GA4 report. Example Analytics Questions: Which key events have the highest conversion rates and total revenue? How do key event completion rates vary across different time periods? What is the average revenue per key event completion?
ga4_export__sessions_enhanced	Enriches session data with engagement metrics, event counts, and timing information to provide comprehensive session-level analytics for understanding user behavior patterns. Example Analytics Questions: What is the average session duration and total events per session? Which sessions result in high engagement or conversion events? How do session start and end times correlate with user engagement levels?

¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

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Prerequisites

To use this dbt package, you must have the following:

• At least one Fivetran GA4 Export connection syncing data into your destination.
• A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Connector Restrictions

This package is designed for connections using the default column sync mode, not the json sync mode. It also assumes the underlying schema corresponds to connector versions synced after July 24, 2023.

For more information on the underlying schema, please refer to the connector docs and Google Analytics' documentation on the Export schema.

Database Incremental Strategies

Many of the models in this package are materialized incrementally, so we have configured our models to work with the different strategies available to each supported warehouse.

For BigQuery and Databricks All Purpose Cluster runtime destinations, we have chosen insert_overwrite as the default strategy, which benefits from the partitioning capability.

For Databricks SQL Warehouse destinations, models are materialized as tables without support for incremental runs.

For Snowflake, Redshift, and Postgres databases, we have chosen delete+insert as the default strategy.

Regardless of strategy, we recommend that users periodically run a --full-refresh to ensure a high level of data quality.

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

• To add the package in the Fivetran dashboard, follow our Quickstart guide.
• To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

Install the package

Include the following ga4_export package version in your packages.yml file:

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.

packages:
  - package: fivetran/ga4_export
    version: [">=0.7.0", "<0.8.0"] # we recommend using ranges to capture non-breaking changes automatically

Define database and schema variables

Option A: Single connection

By default, this package runs using your destination and the ga4_export schema. If this is not where your GA4 Export data is (for example, if your GA4 Export schema is named ga4_export_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    ga4_export_database: your_destination_name
    ga4_export_schema: your_schema_name

Option B: Union multiple connections

If you have multiple GA4 Export connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. For each source table, the package will union all of the data together and pass the unioned table into the transformations. The source_relation column in each model indicates the origin of each record.

To use this functionality, you will need to set the ga4_export_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  ga4_export:
    ga4_export_sources:
      - database: connection_1_destination_name # Required
        schema: connection_1_schema_name # Required
        name: connection_1_source_name # Required only if following the step in the following subsection

      - database: connection_2_destination_name
        schema: connection_2_schema_name
        name: connection_2_source_name

Previous versions of this package employed two separate, mutually exclusive variables for unioning: ga4_export_union_schemas and ga4_export_union_databases. While these variables are still supported, ga4_export_sources is the recommended variable to configure.

Optional: Incorporate unioned sources into DAG

If you use Fivetran Transformations for dbt Core™ and are unioning multiple GA4 Export connections, you can define your sources in a property .yml file, using this as a template. Set the variable has_defined_sources: true under the GA4 Export namespace in your dbt_project.yml. Otherwise, your GA4 Export connections won't appear in your DAG. See the union_connections macro documentation for full configuration details.

(Optional) Additional configurations and data integrity notices

Event Date Range

Because of the typical volume of event data, you may want to limit this package's models to work with a recent date range of your GA4 Export data (however, note that all final models are materialized as incremental tables).

By default, the package looks at all events since January 1, 2024. To change this start date, add the following variable to your dbt_project.yml file:

vars:
  ga4_export:
    ga4_export_date_start: 'yyyy-mm-dd' ## default is '2024-01-01'

Discrepancies Between GA4 Export vs GA4 Reports

It's common to see discrepancies when comparing GA4 exported data, which is used in this package, against GA4 reports (such as the ones in the GA4 UI). This can be due to various reasons, including the UI using sampled data, approximated cardinality for metrics, or the grain at which metrics are aggregated.

For example, your GA4 user_acquisition_first_user_source_medium prebuilt report with the GA4 UI may show a daily event count of 6836 for a certain source and medium while the ga4_export__user_acquisition_first_user_source_medium_report model from this dbt package will show 6765.

For more information on why this may occur, please refer to the below articles (Including the official Google Analytics articles as well as 3rd party blogs):

• BigQuery export vs UI
• GA4 and BigQuery: why might data not match?

Intraday Events

The GA4 Export Connector syncs data from intraday tables throughout the day, and syncs daily tables at the end of the day. Events from the intraday tables are flagged by an is_intraday field in the event table. To avoid duplication and since the models in this package are built upon daily tables, we filter out the intraday events in the staging stg_ga4_export model.

Key Events

According to Google Analytics, a key event is an event that's particularly important to the success of your company. Because a key event may differ across companies, we require you specify your list of these events. Otherwise, the package will assume no key events. This will be applied in the ga4_export__conversions_report model which filters the events_report to just key events. Additionally this will manifest in the key_events field in ga4_export__traffic_acquisition_session_source_medium_report and ga4_export__user_acquisition_first_user_source_medium_report. To configure your key events, add the following variable to your dbt_project.yml file.

vars:
  ga4_export:
    key_events: ['click','trial_signup'] # example key events

Lookback Window

Events can sometimes arrive late. Since many of the models in this package are incremental, by default we look back 7 days to ensure late arrivals are captured while avoiding requiring a full refresh. To change the default lookback window, add the following variable to your dbt_project.yml file:

vars:
  ga4_export:
    lookback_window: number_of_days # default is 7

Changing the Build Schema

By default this package will build the GA4 Export staging models within a schema titled (<target_schema> + _stg_ga4_export) and GA4 Export final models within a schema titled (<target_schema> + ga4_export) in your target database. If this is not where you would like your modeled GA4 Export data to be written to, add the following configuration to your dbt_project.yml file:

models:
    ga4_export:
      +schema: my_new_schema_name # leave blank for just the target_schema
      staging:
        +schema: my_new_schema_name # leave blank for just the target_schema

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

vars:
    ga4_export_<default_source_table_name>_identifier: your_table_name 

Source casing for case-sensitive destinations

By default, the package applies case-insensitive comparisons when resolving source_relation values. If your destination is case-sensitive and you want downstream transformations to respect the exact casing of your source database and schema names, set the following variable:

vars:
    fivetran_using_source_casing: true

(Optional) Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core setup guides.

Does this package have dependencies?

This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

How is this package maintained and can I contribute?

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

Contributions

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.

Are there any resources available?

• If you have questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
• If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.