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Recharge dbt package (Docs)

What does this dbt package do?

  • Produces modeled tables that leverage Recharge data from Fivetran's connector in the format described by this ERD and build off the output of our Recharge source package.
  • Enables you to better understand your Recharge data by summarizing customer, revenue, and subscription trends.
  • Generates a comprehensive data dictionary of your source and modeled Recharge data through the dbt docs site.

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

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

Table Description
recharge__billing_history Each record represents an order, enriched with metrics about related charges and line items. Line items are aggregated at the billing (order) level.
recharge__charge_line_item_history Each record represents a specific line item charge, refund, or other line item that accumulates into final charges.
recharge__customer_daily_rollup Each record provides totals and running totals for a customer's associated transactions for the specified day.
recharge__customer_details Each record represents a customer, enriched with metrics about their associated transactions.
recharge__monthly_recurring_revenue Each record represents a customer, MRR, and non-MRR generated on a monthly basis.
recharge__subscription_overview Each record represents a subscription, enriched with customer and charge information.
recharge__line_item_enhanced This model constructs a comprehensive, denormalized analytical table that enables reporting on key revenue, subscription, customer, and product metrics from your billing platform. It’s designed to align with the schema of the *__line_item_enhanced model found in Recharge, Recurly, Stripe, Shopify, and Zuora, offering standardized reporting across various billing platforms. To see the kinds of insights this model can generate, explore example visualizations in the Fivetran Billing Model Streamlit App. Visit the app for more details.

An example churn model is separately available in the analysis folder:

analysis model description
recharge__account_churn_analysis Each record represents a customer and their churn reason according to recharge's documentation.

Example Visualizations

Curious what these models can do? Check out example visualizations from the recharge__line_item_enhanced model in the Fivetran Billing Model Streamlit App, and see how you can use these models in your own reporting. Below is a screenshot of an example report—-explore the app for more.

Streamlit Billing Model App

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

Step 2: Install the package

Include the following recharge 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/recharge
    version: [">=0.3.0", "<0.4.0"] # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the recharge_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.

Databricks Dispatch Configuration

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

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

Step 3: Define database and schema variables

By default, this package runs using your destination and the recharge schema. If your Recharge data is in a different database or schema (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_destination_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.

Enabling Standardized Billing Model

This package contains the recharge__line_item_enhanced model which constructs a comprehensive, denormalized analytical table that enables reporting on key revenue, subscription, customer, and product metrics from your billing platform. It’s designed to align with the schema of the *__line_item_enhanced model found in Recurly, Recharge, Stripe, Shopify, and Zuora, offering standardized reporting across various billing platforms. To see the kinds of insights this model can generate, explore example visualizations in the Fivetran Billing Model Streamlit App. For the time being, this model is disabled by default. If you would like to enable this model you will need to adjust the recharge__standardized_billing_model_enabled variable to be true within your dbt_project.yml:

vars:
  recharge__standardized_billing_model_enabled: true # false by default.

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.

Setting the date range

By default, the models customer_daily_rollup and monthly_recurring_revenue will aggregate data for the entire date range of your data set. However, you may limit this date range if desired by defining the following variables. You do not need to set both if you only want to limit one.

vars:
    recharge_first_date: "yyyy-mm-dd"
    recharge_last_date: "yyyy-mm-dd"

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 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 builds the Recharge staging models within a schema titled (<target_schema> + _recharge_source) and the Recharge transformation models within a schema titled (<target_schema> + _recharge) in your destination. If this is not where you would like your Recharge data written, add the following configuration to your root dbt_project.yml file:

models:
    recharge:
      +schema: my_new_schema_name # leave blank for just the target_schema
    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/recharge_source
      version: [">=0.3.0", "<0.4.0"]

    - 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 maintains only the latest version of the package. We highly recommend that you consistently use the latest version of the package and refer to the CHANGELOG and release notes for more information about changes.

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.

Opinionated Modelling Decisions

This dbt package takes an opinionated stance on revenue is calculated, using charges in some cases and orders in others. If you would like a deeper explanation of the logic used by default in the dbt package, you may reference the DECISIONLOG.

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.