Microsoft Dynamics 365 CRM dbt Package (Docs)

What does this dbt package do?

This package models Microsoft Dynamics 365 CRM data from Fivetran's connector. It uses data in the format described by this ERD.

The main focus of the package is to enhance the Microsoft Dynamics 365 CRM data by adding human-readable labels for fields (created as <field_name>_label) that store coded values (e.g., integer codes or option sets). This package integrates stringmaps into the source tables, translating codes into meaningful labels.

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

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

Table	Description
account	Model representing accounts in Dynamics 365 CRM, enriched with human-readable column names for fields with corresponding stringmap labels created as <field_name>_label.
appointment	Model representing appointments in Dynamics 365 CRM, enriched with human-readable column names for fields with stringmap labels created as <field_name>_label.
contact	Model for contacts in Dynamics 365 CRM, enriched with human-readable column names for fields with stringmap labels created as <field_name>_label.
incident	Model for incidents in Dynamics 365 CRM, enriched with human-readable column names for fields with stringmap labels created as <field_name>_label.
opportunity	Model for opportunities in Dynamics 365 CRM, enriched with human-readable column names for fields with stringmap labels created as <field_name>_label.
phonecall	Model for phone calls in Dynamics 365 CRM, enriched with human-readable column names for fields with stringmap labels created as <field_name>_label.
systemuser	Model for system users in Dynamics 365 CRM, enriched with human-readable column names for fields with stringmap labels created as <field_name>_label.

Materialized Models

Each Quickstart transformation job run materializes 7 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.

How do I use the dbt package?

Step 1: Prerequisites

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

• At least one Fivetran Microsoft Dynamics 365 CRM connection 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 Microsoft Dynamics 365 CRM 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/dynamics_365_crm
    version: 0.1.0-a1

Step 3: Define database and schema variables

By default, this package runs using your destination and the dynamics_365 schema. If this is not where your Microsoft Dynamics 365 CRM data is (for example, if your Microsoft Dynamics 365 CRM schema is named dynamics_365_crm_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
  dynamics_365_crm_database: your_database_name
  dynamics_365_crm_schema: your_schema_name 

Step 4: Enable/Disable Variables

By default, this package brings in data from the Microsoft Dynamics 365 CRM source tables listed in models/src_dynamics_365_crm.yml. However, if you have disabled syncing any of these sources, you will need to add the following configuration to your dbt_project.yml:

vars:
    dynamics_365_crm_using_<default_source_table_name>: false # default = true

(Optional) Step 5: Additional configurations

Expand/Collapse details

Changing the Build Schema

By default this package will build the Microsoft Dynamics 365 CRM models within a schema titled (<target_schema> + _dynamics_365_crm) in your target database. If this is not where you would like your modeled Microsoft Dynamics 365 CRM data to be written, add the following configuration to your dbt_project.yml file:

models:
  dynamics_365_crm:
    +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:

vars:
    dynamics_365_crm_<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: 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.