https://github.com/fivetran/dbt_iterable_source
Fivetran data source models for Iterable using dbt.
https://github.com/fivetran/dbt_iterable_source
dbt dbt-packages fivetran iterable
Last synced: 6 months ago
JSON representation
Fivetran data source models for Iterable using dbt.
- Host: GitHub
- URL: https://github.com/fivetran/dbt_iterable_source
- Owner: fivetran
- License: apache-2.0
- Created: 2021-03-26T00:20:41.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-03-03T15:38:22.000Z (7 months ago)
- Last Synced: 2025-03-29T05:33:44.164Z (6 months ago)
- Topics: dbt, dbt-packages, fivetran, iterable
- Language: Shell
- Homepage: https://fivetran.github.io/dbt_iterable_source/
- Size: 1.62 MB
- Stars: 2
- Watchers: 39
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# Iterable Source dbt Package ([docs](https://fivetran.github.io/dbt_iterable_source/))
## What does this dbt package do?
- Materializes [Iterable staging tables](https://fivetran.github.io/dbt_iterable_source/#!/overview/iterable_source/models/?g_v=1&g_e=seeds) which leverage data in the format described by [this ERD](https://fivetran.com/docs/applications/iterable#schemainformation). These staging tables clean, test, and prepare your Iterable data from [Fivetran's connector](https://fivetran.com/docs/applications/iterable) for analysis by doing the following:
- Adds descriptions to tables and columns that are synced using Fivetran
- Models staging tables, which will be used in our transform package
- Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
- Generates a comprehensive data dictionary of your source and modeled Iterable data through the [dbt docs site](https://fivetran.github.io/dbt_iterable_source/).
- These tables are designed to work simultaneously with our [Iterable transformation package](https://github.com/fivetran/dbt_iterable/).
> This package does not apply freshness tests.
## 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 connection syncing data into your destination.
- A **BigQuery**, **Snowflake**, **Redshift**, **PostgreSQL**, or **Databricks** destination.
##### Unsubscribe tables are no longer history tables
For connections 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](https://hub.getdbt.com/) for the latest installation instructions or [read the dbt docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages.
```yaml
packages:
- package: fivetran/iterable_source
version: [">=0.10.0", "<0.11.0"]
```
### Step 3: Define database and schema variables
By default, this package runs using your destination and the `iterable` schema of your [target database](https://docs.getdbt.com/docs/running-a-dbt-project/using-the-command-line-interface/configure-your-profile). If this is not where your Iterable data is (for example, if your Iterable schema is named `iterable_fivetran`), add the following configuration to your root `dbt_project.yml` file:
```yml
vars:
iterable_database: your_database_name
iterable_schema: your_schema_name
```
### Step 4: Enabling/Disabling Models
Your Iterable connection 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`.
```yml
vars:
iterable__using_campaign_label_history: false # default is true
iterable__using_user_unsubscribed_message_type: 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.
**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:
```yml
# 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 Iterable staging models within a schema titled ( + `_stg_iterable`) in your target database. If this is not where your would like you Iterable staging data to be written to, add the following configuration to your `dbt_project.yml` file:
```yml
models:
iterable_source:
+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 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`](https://github.com/fivetran/dbt_iterable_source/blob/main/dbt_project.yml) variable declarations to see the expected names.
```yml
vars:
iterable__identifier: "your_table_name"
```
#### 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:
```yml
vars:
iterable_campaign_suppression_list_history_identifier: "campaign_supression_list_history"
```
### (Optional) Step 6: 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™](https://fivetran.com/docs/transformations/dbt). Learn how to set up your project for orchestration through Fivetran in our [Transformations for dbt Core setup guides](https://fivetran.com/docs/transformations/dbt#setupguide).
## 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](https://hub.getdbt.com/) 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.
```yml
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](https://hub.getdbt.com/fivetran/iterable_source/latest/) of the package and refer to the [CHANGELOG](https://github.com/fivetran/dbt_iterable_source/blob/main/CHANGELOG.md) 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](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) 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](https://github.com/fivetran/dbt_iterable_source/issues/new/choose) 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](https://www.surveymonkey.com/r/DQ7K7WW).