Zendesk Support Source dbt Package (Docs)

📣 What does this dbt package do?

• Materializes Zendesk Support staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Zendesk Support 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 Support data through the dbt docs site.
• These tables are designed to work simultaneously with our Zendesk Support transformation package.

🎯 How do I use the dbt package?

Step 1: Prerequisites

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

• A Fivetran Zendesk Support connector syncing data into your destination.
• A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Databricks Dispatch Configuration

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']

Step 2: Install the package

Include the following zendesk_source package version in your packages.yml file only if you are NOT also installing the Zendesk Support 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: [">=0.11.0", "<0.12.0"]

Step 3: Define database and schema variables

By default, this package runs using your target database and the zendesk schema. If this is not where your Zendesk Support 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 

Step 4: Disable models for non-existent sources

This package takes into consideration that not every Zendesk Support 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

(Optional) Step 5: Additional configurations

Add passthrough columns

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]

Mark Former Internal Users as Agents

If a team member leaves your organization and their internal account is deactivated, their USER.role will switch from agent or admin to end-user. This will skew historical ticket SLA metrics, as we calculate reply times and other metrics based on agent or admin activity only.

To persist the integrity of historical ticket SLAs and mark these former team members as agents, provide the internal_user_criteria variable with a SQL clause to identify them, based on fields in the USER table. This SQL will be wrapped in a case when statement in the stg_zendesk__user model.

Example usage:

# dbt_project.yml
vars:
  zendesk_source:
    internal_user_criteria: "lower(email) like '%@fivetran.com' or external_id = '12345' or name in ('Garrett', 'Alfredo')" # can reference any non-custom field in USER

Change the build schema

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 Support 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

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:
    zendesk_<default_source_table_name>_identifier: your_table_name 

🚨 Snowflake Users

If you do not use the default all-caps naming conventions for Snowflake, you may need to provide the case-sensitive spelling of your source tables that are also Snowflake reserved words.

In this package, this would apply to the GROUP source. If you are receiving errors for this source, include the below identifier in your dbt_project.yml file:

vars:
    zendesk_group_identifier: "Group" # as an example, must include the double-quotes and correct case!

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

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.

🔍 Does this package have dependencies?

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 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: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

🙌 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 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.

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 to learn how to contribute to a dbt package!

🏪 Are there any resources available?

• 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 be part of the community discourse? Create a post in the Fivetran community and our team along with the community can join in on the discussion!