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

This package enriches your Fivetran data by doing the following:

• Adds descriptions to tables and columns that are synced using Fivetran
• 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.
• Models staging tables, which will be used in our transform package

This package contains staging models, designed to work simultaneously with our Salesforce modeling package. The staging models:

• Remove any rows that are soft-deleted
• Name columns consistently across all packages:
  • Boolean fields are prefixed with is_ or has_
  • Timestamps are appended with _at
  • ID primary keys are prefixed with the name of the table. For example, the user table's ID column is renamed user_id.

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

By default, this package will run using your target database and the salesforce schema. If this is not where your Salesforce data is (perhaps your Salesforce schema is salesforce_fivetran), add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
vars:
  salesforce_source:
    salesforce_database: your_database_name
    salesforce_schema: your_schema_name

This package includes all source columns defined in the generate_columns.sql macro. To add additional columns to this package, do so using our pass-through column variables. This is extremely useful if you'd like to include custom fields to the package.

# dbt_project.yml

...
vars:
  salesforce_source:
    account_pass_through_columns: [account_custom_field_1, account_custom_field_2]
    opportunity_pass_through_columns: [my_opp_custom_field]
    user_pass_through_columns: [users_have_custom_fields_too, lets_add_them_all]

Additional contributions to this package are very welcome! Please create issues or open PRs against master. Check out this post on the best workflow for contributing to a package.

This package has been tested on BigQuery, Snowflake and Redshift.

Coming soon -- compatibility with Spark

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