ServiceNow dbt Package (Docs)

What does this dbt package do?

This package models ServiceNow data from Fivetran's connector. It uses data in the format described by this ERD.

The main focus of the package is to transform the core object tables into analytics-ready models, including:

• Materializes ServiceNow staging and output models which leverage data in the format described by this ERD.
• The staging tables clean, test, and prepare your ServiceNow data from Fivetran's connector for analysis by doing the following:
  • Renames fields for consistency and standardization. For example, primary keys sys_id are renamed to <table_name>_id.
  • 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 ServiceNow data through the dbt docs site.
• The output models are focused around task management.
  • Summarizes task, problem, change, incident, and change request data by varying grains including last updated day, status, priority, impact, and urgency.
  • Enhances each task record with additional information about its associated problem, incident, or change request. Finally, it contains user information for who opened, started, updated, reported, fixed, closed, requested, reported, or confirmed the task.
  • Enhances user data with associated roles and groups.

The output models are focused around the task management feature of ServiceNow in addition to enhancing user records.

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

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

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.

How do I use the dbt package?

Step 1: Prerequisites

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

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

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']

Step 2: 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: [">=0.3.0", "<0.4.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define database and schema variables

Single connector

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

# dbt_project.yml

vars:
    servicenow_database: your_database_name
    servicenow_schema: your_schema_name

Union multiple connectors

If you have multiple ServiceNow connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either the servicenow_union_schemas OR servicenow_union_databases variables (cannot do both) in your root dbt_project.yml file:

# dbt_project.yml

vars:
    servicenow_union_schemas: ['servicenow_usa','servicenow_canada'] # use this if the data is in different schemas/datasets of the same database/project
    servicenow_union_databases: ['servicenow_usa','servicenow_canada'] # use this if the data is in different databases/projects but uses the same schema name

NOTE: The native source.yml connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined source.yml.

To connect your multiple schema/database sources to the package models, follow the steps outlined in the Union Data Defined Sources Configuration section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.

(Optional) Step 4: Additional configurations

Populating the User Models

Our user grain models are disabled by default because not everyone syncs the underlying tables: sys_user_role, sys_user_has_role, and sys_user_grmember. If these tables do exist in your schema, set this following variable servicenow__using_roles to True in order to populate the user models servicenow__user_aggregated and servicenow__user_enhanced.

# dbt_project.yml

vars:
    servicenow__using_roles: True

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 

(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™

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. Check out this dbt Discourse article on the best workflow for contributing to a 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.