Zendesk Source dbt Package (Docs)
- Materializes Zendesk staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Zendesk data from Fivetran's connector for analysis by doing the following:
- Name columns for consistency across all packages and for easier analysis
- Adds freshness tests to source data
- 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 Zendesk data through the dbt docs site.
- These tables are designed to work simultaneously with our Zendesk transformation package.
To use this dbt package, you must have the following:
- A Fivetran Zendesk connector syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your dbt_project.yml
. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils
then the dbt-labs/dbt_utils
packages respectively.
dispatch:
- macro_namespace: dbt_utils
search_order: ['spark_utils', 'dbt_utils']
Include the following zendesk_source package version in your packages.yml
file only if you are NOT also installing the Zendesk transformation package. The transform package has a dependency on this source package.
TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.
packages:
- package: fivetran/zendesk_source
version: [">=0.8.0", "<0.9.0"]
By default, this package runs using your target database and the zendesk
schema. If this is not where your Zendesk data is (for example, if your zendesk schema is named zendesk_fivetran
), add the following configuration to your root dbt_project.yml
file:
vars:
zendesk_database: your_destination_name
zendesk_schema: your_schema_name
If you cannot hard-code your database or schema, you can provide the names of environment variables that contain these values:
vars:
zendesk_database_env_var: your_destination_name
zendesk_schema_env_var: your_schema_name
If _env_var
values are set, they will be used before falling back to hard-coded var
values.
This package takes into consideration that not every Zendesk account utilizes the schedule
, domain_name
, user_tag
, organization_tag
, or ticket_form_history
features, and allows you to disable the corresponding functionality. By default, all variables' values are assumed to be true
. Add variables for only the tables you want to disable:
vars:
using_schedules: False #Disable if you are not using schedules
using_domain_names: False #Disable if you are not using domain names
using_user_tags: False #Disable if you are not using user tags
using_ticket_form_history: False #Disable if you are not using ticket form history
using_organization_tags: False #Disable if you are not using organization tags
Expand to view configurations
This package includes all source columns defined in the staging models. However, the stg_zendesk__ticket
model allows for additional columns to be added using a pass-through column variable. This is extremely useful if you'd like to include custom fields to the package.
vars:
zendesk__ticket_passthrough_columns: [account_custom_field_1, account_custom_field_2]
By default, this package builds the zendesk staging models within a schema titled (<target_schema>
+ _zendesk_source
) in your target database. If this is not where you would like your Zendesk staging data to be written to, add the following configuration to your root dbt_project.yml
file:
models:
zendesk_source:
+schema: my_new_schema_name # leave blank for just the target_schema
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:
zendesk_<default_source_table_name>_identifier: your_table_name
Expand to view 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.
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: dbt-labs/spark_utils
version: [">=0.3.0", "<0.4.0"]
The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend that 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 to learn how to contribute to a dbt package!
- If you have questions or want to reach out for help, please refer to 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.
- Have questions or want to just say hi? Book a time during our office hours on Calendly or email us at solutions@fivetran.com.