Intercom Source dbt Package (Docs)

NOTE: Our Intercom model and source dbt packages only work with connectors that were created in July 2020 or later. If you created your connector before July 2020, you must set up a new Intercom connector to use these dbt packages.

What does this dbt package do?

Materializes Intercom staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Intercom data from Fivetran's connector for analysis by doing the following:
- Names columns for consistency across all packages and for easier analysis
- Adds freshness tests to source data
- 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 intercom data through the dbt docs site.
These tables are designed to work simultaneously with our Intercom transformation package.

How do I use the dbt package?

Step 1: Prerequisites

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

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

Step 2: Install the package

Include the following intercom_source 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/intercom_source
    version: [">=0.8.0", "<0.9.0"]

Step 3: Define database and schema variables

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

vars:
    intercom_database: your_database_name
    intercom_schema: your_schema_name

(Optional) Step 4: Additional configurations

Expand for configurations

Passthrough Columns

This package includes all source columns defined in the macros folder. You can add more columns 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-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables in your root dbt_project.yml.

vars:
  intercom__company_history_pass_through_columns: 
    - name: company_history_custom_field
      alias: new_name_for_this_field
      transform_sql: "cast(new_name_for_this_field as int64)"
    - name: this_other_field
      transform_sql: "cast(this_other_field as string)"
    - name: custom_monthly_spend
    - name: custom_paid_subscriber
  # a similar pattern can be applied to the rest of the following variables.
  intercom__contact_history_pass_through_columns:
  intercom__conversation_history_pass_through_columns:

Disabling Models

This package includes Intercom's company tag, contact tag, contact company,conversation tag, team and team admin mapping tables.

It's possible that your connector does not sync every table that this package expects. If your syncs exclude certain tables, it is because you either don't use that functionality or actively excluded some tables from your syncs. To disable the corresponding functionality in the package, you must add the relevant variables. By default, the package assumes that all variables are true. Add variables for only the tables you want to disable.

# dbt_project.yml

...
vars:
  intercom__using_contact_company: False
  intercom__using_company_tags: False
  intercom__using_contact_tags: False
  intercom__using_conversation_tags: False
  intercom__using_team: False

Changing the Build Schema

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

# dbt_project.yml

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

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

Expand for more 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"]

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:
    intercom_<default_source_table_name>_identifier: your_table_name

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 that 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 to learn how to contribute to a dbt 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.