Zendesk Support Modeling dbt Package (Docs)

Enabling the unstructured document model for NLP

This package includes the zendesk__document model, which processes and segments Zendesk text data for vectorization, making it suitable for NLP workflows. The model outputs structured chunks of text with associated document IDs, segment indices, and token counts. For definitions and more information, refer to zendesk__document in our dbt docs.

By default, this model is disabled. To enable it, update the zendesk__unstructured_enabled variable to true in your dbt_project.yml:

vars:
  zendesk__unstructured_enabled: true # false by default.

Customizing Chunk Size for Vectorization

The zendesk__document model was developed to limit approximate chunk sizes to 7,500 tokens, optimized for OpenAI models. However, you can adjust this limit by setting the max_tokens variable in your dbt_project.yml:

vars:
  zendesk_max_tokens: 5000 # Default value

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.

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

Tracking Ticket Field History Columns

The zendesk__ticket_field_history model generates historical data for the columns specified by the ticket_field_history_columns variable. By default, the columns tracked are status, priority, and assignee_id. If you would like to change these columns, add the following configuration to your dbt_project.yml file. Additionally, the zendesk__ticket_field_history model allows for tracking the specified fields updater information through the use of the zendesk_ticket_field_history_updater_columns variable. The values passed through this variable limited to the values shown within the config below. By default, the variable is empty and updater information is not tracked. If you would like to track field history updater information, add any of the below specified values to your dbt_project.yml file. After adding the columns to your root dbt_project.yml file, run the dbt run --full-refresh command to fully refresh any existing models.

vars:
    ticket_field_history_columns: ['the','list','of','column','names']
    ticket_field_history_updater_columns: [
                                            'updater_user_id', 'updater_name', 'updater_role', 'updater_email', 'updater_external_id', 'updater_locale', 
                                            'updater_is_active', 'updater_user_tags', 'updater_last_login_at', 'updater_time_zone', 
                                            'updater_organization_id', 'updater_organization_domain_names' , 'updater_organization_organization_tags'
                                            ]

Note: This package only integrates the above ticket_field_history_updater_columns values. If you'd like to include additional updater fields, please create an issue specifying which ones.

Extending and Limiting the Ticket Field History

This package will create a row in zendesk__ticket_field_history for each day that a ticket is open, starting at its creation date. A Zendesk Support ticket cannot be altered after being closed, so its field values will not change after this date. However, you may want to extend a ticket's history past its closure date for easier reporting and visualizing. To do so, add the following configuration to your root dbt_project.yml file:

# dbt_project.yml

vars:
  zendesk:
    ticket_field_history_extension_months: integer_number_of_months # default = 0 

Conversely, you may want to only track the past X years of ticket field history. This could be for cost reasons, or because you have a BigQuery destination and have over 4,000 days (10-11 years) of data, leading to a too many partitions error in the package's incremental models. To limit the ticket field history to the most recent X years, add the following configuration to your root dbt_project.yml file:

# dbt_project.yml

vars:
  zendesk:
    ticket_field_history_timeframe_years: integer_number_of_years # default = 50 (everything)

Changing the Build Schema

By default this package will build the Zendesk Support staging models within a schema titled (<target_schema> + _zendesk_source), the Zendesk Support intermediate models within a schema titled (<target_schema> + _zendesk_intermediate), and the Zendesk Support final models within a schema titled (<target_schema> + _zendesk) in your target database. If this is not where you would like your modeled Zendesk Support data to be written to, add the following configuration to your root dbt_project.yml file:

models:
  zendesk:
    +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
    sla_policy:
      +schema: my_new_schema_name # leave blank for just the target_schema
    ticket_history:
      +schema: my_new_schema_name # leave blank for just the target_schema
  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 

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