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 (skip if using zendesk transformation 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: [">=0.14.0", "<0.15.0"]

Step 3: Define database and schema variables

Option A: Single connector

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 

Note: When running the package with a single source connector, the source_relation column in each model will be populated with an empty string.

Option B: Union multiple connectors

If you have multiple Zendesk connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. For each source table, the package will union all of the data together and pass the unioned table into the transformations. The source_relation column in each model indicates the origin of each record.

To use this functionality, you will need to set the zendesk_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  zendesk_sources:
    - database: connector_1_destination_name # Required
      schema: connector_1_schema_name # Rquired
      name: connector_1_source_name # Required only if following the step in the following subsection

    - database: connector_2_destination_name
      schema: connector_2_schema_name
      name: connector_2_source_name

Recommended: Incorporate unioned sources into DAG

If you are running the package through Fivetran Transformations for dbt Core™, the below step is necessary in order to synchronize model runs with your Zendesk connectors.

By default, this package defines one single-connector source, called zendesk, which will be disabled if you are unioning multiple connectors. This means that your DAG will not include your Zendesk sources, though the package will run successfully.

To properly incorporate all of your Zendesk connectors into your project's DAG:

1. Define each of your sources in a .yml file in your project. Utilize the following template for the source-level configurations, and, most importantly, copy and paste the table and column-level definitions from the package's src_zendesk.yml file.

# a .yml file in your root project
sources:
  - name: <name> # ex: Should match name in zendesk_sources
    schema: <schema_name>
    database: <database_name>
    loader: fivetran
    loaded_at_field: _fivetran_synced
      
    freshness: # feel free to adjust to your liking
      warn_after: {count: 72, period: hour}
      error_after: {count: 168, period: hour}

    tables: # copy and paste from models/src_zendesk.yml - see https://support.atlassian.com/bitbucket-cloud/docs/yaml-anchors/ for how to use anchors to only do so once

Note: If there are source tables you do not have (see Step 4), still include them in this source definition.

2. Set the has_defined_sources variable (scoped to the zendesk_source package) to True, like such:

# dbt_project.yml
vars:
  zendesk_source:
    has_defined_sources: true

Step 4: Enable/Disable models for non-existent sources

This step is optional if you are unioning multiple connectors together in the previous step. The union_data macro will create empty staging models for sources that are not found in any of your Zendesk schemas/databases. However, you can still leverage the below variables if you would like to avoid this behavior.

This package takes into consideration that not every Zendesk Support account utilizes the schedule, schedule_holiday, ticket_schedule, daylight_time, time_zone, audit_log, 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, except for using_schedule_histories. Add variables for only the tables you want to enable/disable:

vars:
    using_schedule_histories:   True          #Enable if you are using audit_logs for schedule histories
    using_schedules:            False         #Disable if you are not using schedules, which requires source tables ticket_schedule, daylight_time, and time_zone
    using_holidays:             False         #Disable if you are not using schedule_holidays for holidays
    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

Collapse/Expand configurations

Add passthrough columns

This package includes all source columns defined in the macros folder. You can add more columns from the TICKET, USER, and ORGANIZATION tables using our pass-through column variables, which will persist these custom fields to the stg_zendesk__ticket, stg_zendesk__user, and stg_zendesk__organization models, respectively.

These variables allow for the pass-through 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-through fields will be casted accordingly. Use the below format for declaring the respective pass-through variables:

vars:
  zendesk__ticket_passthrough_columns:
    - name: "account_custom_field_1" # required
      alias: "account_1" # optional
      transform_sql: "cast(account_1 as string)" # optional, must reference the alias if an alias is provided (otherwise the original name)
    - name: "account_custom_field_2"
      transform_sql: "cast(account_custom_field_2 as string)"
    - name: "account_custom_field_3"
  zendesk__user_passthrough_columns:
    - name: "internal_app_id_c"
      alias: "app_id"
  zendesk__organization_passthrough_columns:
    - name: "custom_org_field_1"

Note: Earlier versions of this package employed a more rudimentary format for passthrough columns, in which the user provided a list of field names to pass in, rather than a mapping. In the above ticket example, this would be [account_custom_field_1, account_custom_field_2, account_custom_field_3].

This old format will still work, as our passthrough-column macros are all backwards compatible.

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 (only if using a single connector)

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. This is not available when running the package on multiple unioned connectors.

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. 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, 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.