Recharge Source dbt package (Docs)

What does this dbt package do?

Materializes Recharge staging tables, which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Recharge data from Fivetran's connector for analysis by doing the following:
- Naming columns for consistency across all packages and easier analysis
- Adding freshness tests to source data
- Adding 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 Recharge data through the dbt docs site.
These tables are designed to work simultaneously with our Recharge 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 Recharge connector syncing data into your destination
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination

Databricks dispatch configuration

If you are using a Databricks destination with this package, you must add the following dispatch configuration (or a variation of thereof) within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils package, then within the dbt-labs/dbt_utils package, respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Step 2: Install the package (skip if also using the `recharge` transformation package)

If you are not using the Recharge transformation package, include the following package version in your packages.yml file. If you are installing the transform package, the source package is automatically installed as a dependency.

Include the following recharge_source package version in your packages.yml file.

TIP: Check dbt Hub for the latest installation instructions, or read dbt's Package Management documentation for more information on installing packages.

packages:
  - package: fivetran/recharge_source
    version: [">=0.3.0", "<0.4.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define database and schema variables

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

vars:
  recharge_database: your_database_name
  recharge_schema: your_schema_name

Step 4: Enable/disable models and sources

Your Recharge connector may not sync every table that this package expects. If you do not have the CHECKOUT, ONE_TIME_PRODUCT and/or CHARGE_TAX_LINE tables synced, add the corresponding variable(s) to your root dbt_project.yml file to disable these sources:

vars:
  recharge__one_time_product_enabled: false # Disables if you do not have the ONE_TIME_PRODUCT table. Default is True.
  recharge__charge_tax_line_enabled: false # Disables if you do not have the CHARGE_TAX_LINE table. Default is True.
  recharge__checkout_enabled: true # Enables if you do have the CHECKOUT table. Default is False.

(Optional) Step 5: Additional configurations

Expand/collapse section.

Leveraging `orders` vs `order` source

For Fivetran Recharge connectors created on or after June 18, 2024, the ORDER source table has been renamed to ORDERS. Refer to the June 2024 connector release notes for more information.

The package will default to use the ORDERS table if it exists and then ORDER if not. If you have both versions but wish to use the ORDER table instead, you can set the variable recharge__using_orders to false in your dbt_project.yml file.

vars:
  recharge__using_orders: false # default is true, which will use the `orders` version of the source.

Passing Through Additional Columns

This package includes all source columns defined in the macros folder. If you would like to pass through additional columns to the staging models, add the following configurations to your dbt_project.yml file. 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:
    recharge__address_passthrough_columns: 
      - name: "new_custom_field"
        alias: "custom_field_name"
        transform_sql:  "cast(custom_field_name as int64)"
      - name: "a_second_field"
        transform_sql:  "cast(a_second_field as string)"
    # a similar pattern can be applied to the rest of the following variables.
    recharge__charge_passthrough_columns:
    recharge__charge_line_item_passthrough_columns:
    recharge__checkout_passthrough_columns:
    recharge__order_passthrough_columns:
    recharge__order_line_passthrough_columns:
    recharge__subscription_passthrough_columns:
    recharge__subscription_history_passthrough_columns:

Changing the Build Schema

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

models:
    recharge_source:
      +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.

vars:
    recharge_<default_source_table_name>_identifier: your_table_name

Snowflake Users

You may need to provide the case-sensitive spelling of your source tables that are also Snowflake reserved words.

In this package, this would apply to the ORDER source. If you are receiving errors for this source, include the following in your dbt_project.yml file. (Note: This should not be necessary for the ORDERS source table.)

vars:
  recharge_order_identifier: '"Order"' # as an example, must include this quoting pattern and adjust for your exact casing

Note: If you have sources defined in your project's yml, the above will not work. Instead you will need to add the following where your order table is defined in your yml:

sources:
  tables:
    - name: order 
      # Add the below
      identifier: ORDER # Or what your order table is named, being mindful of casing
      quoting:
        identifier: true

(Optional) Step 6: 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"]

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.