Zendesk Support Modeling dbt Package (Docs)

What does this dbt package do?

• Produces modeled tables that leverage Zendesk Support data from Fivetran's connector in the format described by this ERD and build off the output of our zendesk source package.
• Enables you to better understand the performance of your Support team. It calculates metrics focused on response times, resolution times, and work times for you to analyze. It performs the following actions:
  • Creates an enriched ticket model with relevant resolution, response time, and other metrics
  • Produces a historical ticket field history model to see velocity of your tickets over time
  • Converts metrics to business hours for Zendesk Support Professional or Enterprise users
  • Calculates SLA policy breaches for Zendesk Support Professional or Enterprise users
• Generates a comprehensive data dictionary of your source and modeled Zendesk Support data through the dbt docs site.

Note: Tickets from the Zendesk Support Chat channel will not populate in this package as the Fivetran connector does not currently support Chat based tickets. This is a feature request that has been flagged.

The following table provides a detailed list of final tables materialized within this package by default.

TIP: See more details about these tables in the package's dbt docs site.

Table	Description
zendesk__ticket_metrics	Each record represents a Zendesk Support ticket, enriched with metrics about reply times, resolution times, and work times. Calendar and business hours are supported.
zendesk__ticket_enriched	Each record represents a Zendesk Support ticket, enriched with data about its tags, assignees, requester, submitter, organization, and group.
zendesk__ticket_summary	A single record table containing Zendesk Support ticket and user summary metrics.
zendesk__ticket_backlog	A daily historical view of the ticket field values defined in the ticket_field_history_columns variable for all backlog tickets. Backlog tickets being defined as any ticket not in a 'closed', 'deleted', or 'solved' status.
zendesk__ticket_field_history	A daily historical view of the ticket field values defined in the ticket_field_history_columns variable and the corresponding updater fields defined in the ticket_field_history_updater_columns variable.
zendesk__sla_policies	Each record represents an SLA policy event and additional sla breach and achievement metrics. Calendar and business hour SLA breaches are supported.
zendesk__document	Each record represents a chunk of text from ticket data, prepared for vectorization. It includes fields for use in NLP workflows. Disabled by default.

Many of the above reports are now configurable for visualization via Streamlit. Check out some sample reports here.

How do I use the dbt package?

Step 1: Prerequisites

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

• At least one Fivetran zendesk 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 package version in your packages.yml file:

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.

packages:
  - package: fivetran/zendesk
    version: [">=0.18.0", "<0.19.0"]

Note: Do not include the Zendesk Support source package. The Zendesk Support transform package already has a dependency on the source in its own packages.yml file.

Step 3: Define database and schema variables

By default, this package runs using your destination and the zendesk schema. If this is not where your zendesk data is (for example, if your zendesk schema is named zendesk_fivetran), update the following variables in your root dbt_project.yml file accordingly:

vars:
    zendesk_database: your_destination_name
    zendesk_schema: your_schema_name 

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

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

Expand/Collapse details

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 

(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™. 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/zendesk_source
      version: [">=0.13.0", "<0.14.0"]

    - 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"]

    - package: calogica/dbt_date
      version: [">=0.9.0", "<1.0.0"]

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 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 on the best workflow for contributing to a package.

Opinionated Modelling Decisions

This dbt package takes an opinionated stance on how business time metrics are calculated. The dbt package takes all schedules into account when calculating the business time duration. Whereas, the Zendesk Support UI logic takes into account only the latest schedule assigned to the ticket. If you would like a deeper explanation of the logic used by default in the dbt package you may reference the DECISIONLOG.

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.