Fivetran Salesforce Formula Utils

What does this dbt package do?

This package includes macros and scipts to be used within a dbt project to accurately map Salesforce Formulas to existing tables. It is designed to work with data from Fivetran's Salesforce connector in the format described by this ERD.

Note: this package is distinct from the Salesforce dbt package, which transforms Salesforce data and outputs analytics-ready end models.

How do I use the dbt package?

Step 1: Prerequisites

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

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

Step 2: Install the package

The sfdc_formula_view macro is intended to be leveraged within a dbt project that uses the source tables from Fivetran's Salesforce connector. To leverage the macro, you will add the below configuration to your packages.yml file (if you do not have a packages.yml file, create one in your root dbt project).

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

packages:
  - package: fivetran/salesforce_formula_utils
    version: [">=0.10.0", "<0.11.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define required source tables

In order to use the macros included in this package, you will need to have a properly configured Salesforce source named salesforce in your own dbt project. An example of a properly configured Salesforce source yml can be found in the src_salesforce.yml file in integration_tests. This integration_tests folder is just for testing purposes - your source file will need to be in the models folder of your root dbt project. You are welcome to copy/paste the example source configuration into your src_salesforce.yml file and modify for your use case.

In particular, you will need the following sources defined in your src_salesforce.yml file:

version: 2

sources:
  - name: salesforce # It would be best to keep this named salesforce
    schema: "{{ var('salesforce_schema', 'salesforce') }}" # Configure the salesforce_schema var from your dbt_project.yml (alternatively you can hard-code the schema here if only using one Salesforce connector)
    # Add the database where your Salesforce data resides if different from the target database. Eg. 'my_salesforce_database'. By default the target.database is used.
    tables:
      - name: fivetran_formula_model
        description: Contains SQL models produced by Fivetran Salesforce connector

      ## Any other source tables you are creating models for should be defined here as well. They aren't required, but it is best organizational practice and allows Fivetran to compile data lineage graphs

Step 4: Create models

Generate models using connector-made query

To create a model that includes all formula fields:

1. Create a new file in your models folder and name it your_table_name_here.sql (e.g. customer.sql; this is not necessary but recommended as best practice).
2. Add the below snippet calling the sfdc_formula_view macro into the file. Update the source_table argument to be the source table name for which you are generating the model (e.g. customer).

{{ salesforce_formula_utils.sfdc_formula_view(source_table='your_source_table_name_here') }}

Output: All formulas for the chosen source table will be included in the resulting select statement.

Automate model creation

If you have multiple models you need to create, you can also leverage the sfdc_formula_model_automation script within this project to automatically create models locally via the command line. Below is an example command to copy and edit.

source dbt_packages/salesforce_formula_utils/sfdc_formula_model_automation.sh "../path/to/directory" "desired_table_1,desired_table_2,desired_table_infinity"

Output: Model files for each table, populated with {{ salesforce_formula_utils.sfdc_formula_view(source_table='table_name') }}.

Note: In order for this command to work, you must currently be within the root directory of your dbt project.

Step 5: Execute models

Once you have created all your desired models and copied/modified the sql snippet into each model you will execute dbt deps to install the macro package, then execute dbt run to generate the models. Additionally, you can reference the integration_tests folder for examples on how to use the macro within your models.

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

Macro & script documentation

sfdc_formula_view (source)

This macro generates the final sql needed to join the Salesforce formula fields to the desired table.

Usage:

{{ salesforce_formula_utils.sfdc_formula_view(source_table='fivetran_sfdc_example_table') }}

Args:

• source_table (required): The table with which you are joining the formula fields.
• source_name (optional, default = 'salesforce'): The dbt source containing the table you want to join with formula fields (as defined here). Must contain the fivetran_formula_model table.
• using_quoted_identifiers (optional, default = false): For warehouses with case sensitivity enabled this argument must be set to true in order for the underlying macros within this project to properly compile and execute successfully.
• materialization (optional, default = view): By default the model will be materialized as a view. If you would like to materialize as a table, you can adjust using this argument.

sfdc_formula_model_automation.sh (source)

This bash script is intended to be used in order to automatically create the desired salesforce models via the command line within your dbt project. This bash script will generate a model file within your dbt project that contains the sfdc_formula_view macro for the appropriately defined table(s). In order for this command to work you must be within the root directory of your dbt project. Note that this script is designed for dbt Core and is not compatible with dbt Cloud.

Usage:

source dbt_packages/salesforce_formula_utils/sfdc_formula_model_automation.sh "../path/to/directory" "desired_table(s)"

Example Assuming the path to your directory is "../dbt_salesforce" and the table(s) you want to generate the model for are opportunity and account.

source dbt_packages/salesforce_formula_utils/sfdc_formula_model_automation.sh "../dbt_salesforce" "opportunity,account"

Under the hood macros

{{ salesforce_formula_utils.sfdc_formula_pivot(join_to_table='fivetran_sfdc_example_table') }}

{{ salesforce_formula_utils.sfdc_formula_refactor(join_to_table='fivetran_sfdc_example_table',source_name='salesforce') }}

{{ salesforce_formula_utils.sfdc_formula_field_views(join_to_table='fivetran_sfdc_example_table',source_name='salesforce') }}

{{ salesforce_formula_utils.sfdc_formula_view_sql(join_to_table='fivetran_sfdc_example_table',source_name='salesforce') }}

{{ salesforce_formula_utils.sfdc_get_formula_column_values(fivetran_formula='salesforce', key='field', value='sql', join_to_table='fivetran_sfdc_example_table') }}

{{ salesforce_formula_utils.sfdc_star_exact(from=ref('my_model'), relation_alias='my_table', except=["exclude_field_1", "exclude_field_2"]) }}

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 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.