ServiceNow dbt Package

This dbt package transforms data from Fivetran's Servicenow connector into analytics-ready tables.

Resources

• Number of materialized models¹: 39
• Connector documentation
  • ServiceNow connector documentation
  • ServiceNow ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog
• dbt Core™ supported versions
  • >=1.3.0, <3.0.0

What does this dbt package do?

This package enables you to transform core object tables into analytics-ready models and enhance task management data with user information. It creates enriched models with metrics focused on task, problem, change, incident, and change request data.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_servicenow

Final output tables

By default, this package materializes the following final tables:

Table	Description
servicenow__activity_summary	Aggregates IT service management activity across tasks, problems, changes, and incidents by update date and multiple dimensions (state, priority, impact, urgency) including task counts, averages, and SLA metrics for comprehensive operational visibility. Example Analytics Questions: How many critical or high-priority tickets are currently open? What's our average resolution time for urgent issues compared to normal priority? Are we meeting our SLA targets this week across different ticket types?
servicenow__task_enhanced	Tracks tasks with links to related problems, incidents, or change requests, plus comprehensive user activity (opener, assignee, closer), timing metrics, SLA status, and state information to understand task workflows and completion patterns. Example Analytics Questions: What's our average resolution time for high-priority tickets? Do tickets that get reassigned multiple times miss their SLAs? Which types of work (incidents, problems, changes) take longest to resolve?
servicenow__problem_enhanced	Chronicles problems with state, category, user interactions (confirmer, fixer, resolver), timing metrics, known error status, and related task/incident counts to track problem management and resolution effectiveness. Example Analytics Questions: How quickly are we fixing known errors compared to new problems? Which problems are causing the most incidents across our systems? Are major problems being resolved faster than minor ones, or are they reopened more often?
servicenow__incident_enhanced	Provides comprehensive incident records with severity, state, category info, user interactions (caller, resolver, reopener), timing metrics, and problem associations to analyze incident response efficiency and resolution patterns. Example Analytics Questions: How quickly are critical incidents being resolved compared to lower severity issues? Which types of incidents are reopened most often, indicating incomplete resolutions? Do incidents with high business impact get resolved faster than their severity level would suggest?
servicenow__change_request_enhanced	Tracks change requests with user interaction details, change phases, risk levels, review status, timing metrics, and related task counts to monitor change management processes, approval workflows, and implementation efficiency. Example Analytics Questions: How long does it take to start implementing changes after they're requested, especially for high-risk changes? Which types of changes require the most supporting tasks to complete? Are we reviewing and approving changes within our target timeframes?
servicenow__user_aggregated	Summarizes user profiles with group memberships, role assignments, and counts of distinct groups, roles, and included roles to understand team structures, access permissions, and organizational hierarchies. Example Analytics Questions: Which users have the highest count_distinct_sys_user_role_ids and count_distinct_sys_user_group_ids? How are sys_user_role_names distributed across different user groups? What is the average count_distinct_included_roles per user by primary role?
servicenow__user_enhanced	Provides detailed user profiles enriched with group memberships, role assignments, personal info, department, manager, company details, and activity status to enable user-level analysis and access management reporting. Example Analytics Questions: Which users have the most role assignments (count_distinct_sys_user_role_ids) by department_value? How are active versus inactive users (is_active) distributed across companies and departments? What is the relationship between manager_name hierarchies and role assignments?

¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

---

Prerequisites

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

• At least one Fivetran ServiceNow connection syncing data into your destination.
• A BigQuery, Snowflake, Redshift, Databricks, or PostgreSQL destination.

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

• To add the package in the Fivetran dashboard, follow our Quickstart guide.
• To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

Install the package

Include the following ServiceNow 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/servicenow
    version: [">=1.0.0", "<1.1.0"] # we recommend using ranges to capture non-breaking changes automatically

Databricks dispatch configuration

If you are using a Databricks destination with this package, you must add the following (or a variation of the following) 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']

Define database and schema variables

Option A: Single connection

By default, this package runs using your destination and the servicenow schema. If this is not where your Service Now data is (for example, if your Service Now schema is named servicenow_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    servicenow_database: your_destination_name
    servicenow_schema: your_schema_name

Option B: Union multiple connections

If you have multiple Service Now connections 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 servicenow_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  servicenow:
    servicenow_sources:
      - database: connection_1_destination_name # Required
        schema: connection_1_schema_name # Required
        name: connection_1_source_name # Required only if following the step in the following subsection

      - database: connection_2_destination_name
        schema: connection_2_schema_name
        name: connection_2_source_name

Previous versions of this package employed two separate, mutually exclusive variables for unioning: servicenow_union_schemas and servicenow_union_databases. While these variables are still supported, servicenow_sources is the recommended variable to configure.

Optional: Incorporate unioned sources into DAG

If you use Fivetran Transformations for dbt Core™ and are unioning multiple Service Now connections, you can define your sources in a property .yml file, using this as a template. Set the variable has_defined_sources: true under the Service Now namespace in your dbt_project.yml. Otherwise, your Service Now connections won't appear in your DAG. See the union_connections macro documentation for full configuration details.

(Optional) Additional configurations

Enable/Disable the User End Models

Our user grain models are enabled by default, but we do understand that not everyone syncs the underlying tables: sys_user_role, sys_user_has_role, and sys_user_grmember. If these tables do not exist in your schema, set this following variable servicenow__using_roles to False in order to disable the end models servicenow__user_aggregated and servicenow__user_enhanced.

# dbt_project.yml

vars:
    servicenow__using_roles: False # Disable if you are not using user roles

Changing the Build Schema

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

# dbt_project.yml

models:
  servicenow:
    +schema: my_new_schema_name # leave blank for just the target_schema
    staging:
        +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.

# dbt_project.yml

vars:
    servicenow_<default_source_table_name>_identifier: your_table_name 

Source casing for case-sensitive destinations

By default, the package applies case-insensitive comparisons when resolving source_relation values. If your destination is case-sensitive and you want downstream transformations to respect the exact casing of your source database and schema names, set the following variable:

vars:
    fivetran_using_source_casing: true

(Optional) 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/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

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. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.

Opinionated Modelling Decisions

ServiceNow tables can be complex, for example exhibiting many-to-many relationships. For more information on table relationships and how they informed our model development, you may refer to 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.