Produces modeled tables that leverage Iterable data from Fivetran's connector in the format described by this ERD and builds off the output of our Iterable source package.
This package enables you to understand the efficacy of your growth marketing and customer engagement campaigns across email, SMS, push notification, and in-app platforms. The package achieves this by:
EVENT
table with data regarding associated users, campaigns, and channels.LIST_USER_HISTORY
table. The table can be disabled from connector syncs but is required to connect users and their lists.Generates a comprehensive data dictionary of your source and modeled Iterable data through the dbt docs site.
The following table provides a detailed list of all models materialized within this package by default.
TIP: See more details about these models in the package's dbt docs site.
Model | Description |
---|---|
iterable__events | Each record represents a unique event in Iterable, enhanced with information regarding attributed campaigns, the triggering user, and the channel, template, and message type associated with the event. Commerce events are not tracked by the Fivetran connector. See the tracked events details. |
iterable__user_campaign | Each record represents a unique user-campaign-experiment variation combination, enriched with pivoted-out metrics reflecting instances of the user triggering different types of events in campaigns. |
iterable__campaigns | Each record represents a unique campaign-experiment variation, enriched with gross event and unique user interaction metrics, and information regarding templates, labels, and applied or suppressed lists. |
iterable__users | Each record represents the most current state of a unique user, enriched with metrics around the campaigns and lists they have been a part of and interacted with, channels and message types they've unsubscribed from, and more. |
iterable__list_user_history | Each record represents a unique user-list combination. This is intended to recreate the LIST_USER_HISTORY source table, which can be disconnected from your syncs, as it can lead to excessive MAR usage. |
iterable__user_unsubscriptions | Each row represents a message type that a user is currently unsubscribed to, including the channel the message type belongs to. If a user is unsubscribed from an entire channel, each of the channel's message types appears as an unsubscription. |
To use this dbt package, you must have the following:
dbt-databricks
adapter over dbt-spark
because each adapter handles incremental models differently. If you must use the dbt-spark
adapter and run into issues, please refer to this section found in dbt's documentation of Spark configurations.Some of the end models in this package are materialized incrementally. We have chosen insert_overwrite
as the default strategy for BigQuery and Databricks databases, as it is only available for these dbt adapters. For Snowflake, Redshift, and Postgres databases, we have chosen delete+insert
as the default strategy.
insert_overwrite
is our preferred incremental strategy because it will be able to properly handle updates to records that exist outside the immediate incremental window. That is, because it leverages partitions, insert_overwrite
will appropriately update existing rows that have been changed upstream instead of inserting duplicates of them--all without requiring a full table scan.
delete+insert
is our second-choice as it resembles insert_overwrite
but lacks partitions. This strategy works most of the time and appropriately handles incremental loads that do not contain changes to past records. However, if a past record has been updated and is outside of the incremental window, delete+insert
will insert a duplicate record. 😱
Because of this, we highly recommend that Snowflake, Redshift, and Postgres users periodically run a
--full-refresh
to ensure a high level of data quality and remove any possible duplicates.
For connectors created past August 2023, the user_unsubscribed_channel_history
and user_unsubscribed_message_type_history
Iterable objects will no longer be history tables as part of schema changes following Iterable's API updates. The fields have also changed. There is no lift required, since we have checks in place that will automatically persist the respective fields depending on what exists in your schema (they will still be history tables if you are using the old schema).
Please be sure you are syncing them as either both history or non-history.
Include the following Iterable 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/iterable
version: [">=0.11.0", "<0.12.0"]
By default, this package runs using your destination and the iterable
schema of your target database. If this is not where your Iterable data is located (for example, if your Iterable schema is named iterable_fivetran
), add the following configuration to your root dbt_project.yml
file:
vars:
iterable_database: your_database_name
iterable_schema: your_schema_name
Your Iterable connector might not sync every table that this package expects. If your syncs exclude certain tables, it is either because you do not use that functionality in Iterable or have actively excluded some tables from your syncs. In order to enable or disable the relevant tables in the package, you will need to add the following variable(s) to your dbt_project.yml
file.
By default, all variables are assumed to be true
.
vars:
iterable__using_campaign_label_history: false # default is true
iterable__using_user_unsubscribed_message_type_history: false # default is true
iterable__using_campaign_suppression_list_history: false # default is true
This dbt package is dependent on the following dbt packages. Please be aware that 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 rootpackages.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"]
- package: fivetran/iterable_source
version: [">=0.8.0", "<0.9.0"]
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.
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. Check out this dbt Discourse article on the best workflow for contributing to a package!