Iterable Transformation dbt Package (docs)

What does this dbt package do?

• 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:

  • Enriching the core EVENT table with data regarding associated users, campaigns, and channels.
  • Creating current-state models of campaigns and users, enriched with aggregated event and interaction metrics.
  • Creating a current-state model of message types and channels that each user is currently unsubscribed from.
  • Re-creating the 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 tables materialized within this package by default.

TIP: See more details about these models in the package's dbt docs site.

How do I use the dbt package?

Step 1: Prerequisites

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

• At least one Fivetran Iterable connector syncing data into your destination.
• A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Databricks Configuration

• Databricks Runtime 12.2 or later is required to run all models in this package.
• We also recommend using the 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.

Database Incremental Strategies

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.

Unsubscribe tables are no longer history tables

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.

Step 2: Install the package

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: [">=0.13.0", "<0.14.0"]

Step 3: Define database and schema variables

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 

Step 4: Enabling/Disabling Models

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   
    iterable__using_event_extension: false         # default is true   

(Optional) Step 5: Additional configurations

Passing Through Additional Fields

This package includes fields we judged were standard across Iterable users. However, the Fivetran connector allows for additional columns to be brought through in the event_extension and user_history objects. Therefore, if you wish to bring them through, leverage our passthrough column variables. For event_extension columns, ensure that iterable__using_event_extension is set to True, which is the default.

You will see these additional columns populate in the end iterable__list_user_history, iterable__events, and iterable__users models.

Notice: A dbt run --full-refresh is required each time these variables are edited.

These variables allow for the passthrough fields to be aliased (alias) and casted (transform_sql) if desired, but not required. Datatype casting is configured via a sql snippet within the transform_sql key. You may add the desired sql while omitting the as field_name at the end and your custom pass-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables:

# dbt_project.yml

vars:
  iterable_event_extension_pass_through_columns:
    - name: "event_extension_field"
      alias: "renamed_field"
      transform_sql: "cast(renamed_field as string)"
  iterable_user_history_pass_through_columns:
    - name: "user_attribute"
      alias: "renamed_user_attribute"
    - name: "user_attribute_2"

Changing the Build Schema

By default, this package will build the following Iterable models within the schemas below in your target database:

• Final models within a schema titled (<target_schema> + _iterable)
• Intermediate models in (<target_schema> + _int_iterable)
• Staging models within a schema titled (<target_schema> + _stg_iterable)

If this is not where you would like your modeled Iterable data to be written to, add the following configuration to your dbt_project.yml file:

models:
  iterable:
    +schema: my_new_schema_name # leave blank for just the target_schema
    intermediate:
      +schema: my_new_schema_name # leave blank for just the target_schema
  iterable_source:
    +schema: my_new_schema_name # leave blank for just the target_schema

Note: If your profile does not have permissions to create schemas in your destination, you can set each +schema to blank. The package will then write all tables to your pre-existing target schema.

Change the source table references

If an individual source table has a different name than what 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:
    iterable_<default_source_table_name>_identifier: "your_table_name"

Pivoting out event metrics

In the iterable__user_campaign model, there are metrics calculated based on Iterable events. By default, all the below metrics are enabled by default. If not all metrics apply to your use case, you can specify which event metrics to include by adjusting the iterable__event_metrics variable in your own dbt_project.yml.

vars:
    iterable__event_metrics:
    - "emailClick"
    - "emailUnSubscribe"
    - "emailComplaint"
    - "customEvent"
    - "emailSubscribe"
    - "emailOpen"
    - "pushSend"
    - "smsBounce"
    - "pushBounce"
    - "inAppSendSkip"
    - "smsSend"
    - "inAppSend"
    - "pushOpen"
    - "emailSend"
    - "pushSendSkip"
    - "inAppOpen"
    - "emailSendSkip"
    - "emailBounce"
    - "inAppClick"
    - "pushUninstall"

Lookback Window

Records from the source can sometimes arrive late. Since several of the models in this package are incremental, by default we look back 7 days to ensure late arrivals are captured while avoiding the need for frequent full refreshes. While the frequency can be reduced, we still recommend running dbt --full-refresh periodically to maintain data quality of the models.

To change the default lookback window, add the following variable to your dbt_project.yml file:

vars:
  iterable:
    iterable_lookback_window: number_of_days # default is 7

Deprecated CAMPAIGN_SUPRESSION_LIST_HISTORY table

The Iterable connector schema originally misspelled the CAMPAIGN_SUPPRESSION_LIST_HISTORY table as CAMPAIGN_SUPRESSION_LIST_HISTORY (note the singular P). As of August 2021, Fivetran has deprecated the misspelled table and will only continue syncing the correctly named CAMPAIGN_SUPPRESSION_LIST_HISTORY table.

By default, this package refers to the new table (CAMPAIGN_SUPPRESSION_LIST_HISTORY). To change this so that the package works with the old misspelled source table (we do not recommend this, however), add the following configuration to your dbt_project.yml file:

vars:
    iterable_campaign_suppression_list_history_identifier: "campaign_supression_list_history"

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

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"]

    - package: fivetran/iterable_source
      version: [">=0.10.0", "<0.11.0"]

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. Check out this dbt Discourse article on the best workflow for contributing to a package.

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.