Zuora dbt Package

This dbt package transforms data from Fivetran's Zuora connector into analytics-ready tables.

Resources

• Number of materialized models¹: 51
• Connector documentation
  • Zuora connector documentation
  • Zuora ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog

What does this dbt package do?

This package enables you to enhance balance transaction entries with useful fields, create customized churn analysis, and develop monthly recurring revenue insights. It creates enriched models with metrics focused on account activity, subscription management, and billing transaction history.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_zuora

Final output tables

By default, this package materializes the following final tables:

Table	Description
zuora__account_daily_overview	Provides daily account snapshots with invoice counts, amounts, payments, taxes, discounts, refunds, and rolling totals to track account financial health, payment activity, and balance evolution over time. For an example of how this data can be used to evaluate churn at the monthly level, see this analysis query, and see the DECISIONLOG for a more detailed explanation of churn definitions and additonal evaluation grains. Example Analytics Questions: How does rolling_invoice_amount_unpaid evolve day-by-day for each account? What are the daily trends in daily_invoice_amount versus daily_invoice_amount_paid? Which accounts have the highest rolling_invoices and rolling_refunds totals?
zuora__account_overview	Consolidates account profiles with comprehensive transaction metrics including invoice counts, amounts, payments, subscriptions, taxes, discounts, refunds, and monthly averages to understand account financial performance and relationships. Example Analytics Questions: Which accounts have the highest total_invoice_amount and active_subscription_count? How do total_amount_paid versus total_amount_not_paid compare by account_status? What is the monthly_average_invoice_amount and account_zuora_calculated_mrr by customer tenure (account_active_months)?
zuora__billing_history	Tracks invoice-level billing history with amounts, payment details, charges, tax, discounts, refunds, credit adjustments, and product/subscription counts to analyze billing patterns, payment status, and revenue recognition timing. Example Analytics Questions: What is the average invoice_amount and invoice_amount_unpaid by status and due_date aging? How many invoice_items, products, and subscriptions are typically on each invoice? What is the time between invoice_date and first_payment_date by account?
zuora__line_item_history	Chronicles individual invoice line items with product details, subscription info, charge amounts, discounts, taxes, service periods, and revenue calculations to provide granular visibility into revenue components and product-level billing. Example Analytics Questions: Which products (product_id, sku) generate the highest gross_revenue and net_revenue? How do charge_amount and discount_amount vary by charge_type and charge_model? What is the charge_mrr distribution across subscriptions and rate plans?
zuora__monthly_recurring_revenue	Tracks monthly recurring revenue (MRR) and non-MRR by account with gross, discount, and net calculations comparing current to previous month to measure subscription business health, analyze revenue trends, and calculate MRR movement by type. Example Analytics Questions: What is the net_current_month_mrr by account and how does it compare to net_previous_month_mrr? How does MRR break down by net_mrr_type (new, expansion, contraction, churn)? What is the ratio of net_current_month_mrr to gross_current_month_non_mrr by account?
zuora__subscription_overview	Provides detailed subscription profiles with activation dates, term lengths, subscription status, auto-renewal settings, charge details including MRR, amendment info, and period calculations to monitor subscription lifecycle and financial contribution. Example Analytics Questions: Which subscriptions have the longest subscription_days and highest charge_mrr values? How do subscriptions with auto_renew = true compare to false in terms of term lengths? What is the average current_term, initial_term, and renewal_term by status and term_type?
zuora__line_item_enhanced	This table is 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 table found in Zuora, Recharge, Stripe, Shopify, and Recurly, offering standardized reporting across various billing platforms. To see the kinds of insights this table can generate, explore example visualizations in the Fivetran Billing Model Streamlit App. Visit the app for more details and refer to these docs for how to enable the table, which is disabled by default.

¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

---

Visualizations

Many of the above reports are now configurable for visualization via Streamlit. Check out some sample reports here.

Prerequisites

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

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

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

• To add the package in the Fivetran dashboard, follow our Quickstart guide.
• To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

Install the package

Include the following zuora 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/zuora
    version: [">=1.3.0", "<1.4.0"]

All required sources and staging models are now bundled into this transformation package. Do not include fivetran/zuora_source in your packages.yml since this package has been deprecated.

Databricks Dispatch Configuration

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

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

Define database and schema variables

Option A: Single connection

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

vars:
  zuora:
    zuora_database: your_database_name
    zuora_schema: your_schema_name

Option B: Union multiple connections

If you have multiple Zuora connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. For each source table, the package will union all of the data together and pass the unioned table into the transformations. The source_relation column in each model indicates the origin of each record.

To use this functionality, you will need to set the zuora_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  zuora:
    zuora_sources:
      - database: connection_1_destination_name # Required
        schema: connection_1_schema_name # Required
        name: connection_1_source_name # Required only if following the step in the following subsection

      - database: connection_2_destination_name
        schema: connection_2_schema_name
        name: connection_2_source_name

Recommended: Incorporate unioned sources into DAG

If you are running the package through Fivetran Transformations for dbt Core™, the below step is necessary in order to synchronize model runs with your Zuora connections. Alternatively, you may choose to run the package through Fivetran Quickstart, which would create separate sets of models for each Zuora source rather than one set of unioned models.

By default, this package defines one single-connection source, called zuora, which will be disabled if you are unioning multiple connections. This means that your DAG will not include your Zuora sources, though the package will run successfully.

To properly incorporate all of your Zuora connections into your project's DAG:

1. Define each of your sources in a .yml file in your project. Utilize the following template for the source-level configurations, and, most importantly, copy and paste the table and column-level definitions from the package's src_zuora.yml file.

# a .yml file in your root project

version: 2

sources:
  - name: <name> # ex: Should match name in zuora_sources
    schema: <schema_name>
    database: <database_name>
    loader: fivetran
    config:
      loaded_at_field: _fivetran_synced
    freshness: # feel free to adjust to your liking
      warn_after: {count: 72, period: hour}
      error_after: {count: 168, period: hour}

    tables: # copy and paste from zuora/models/staging/src_zuora.yml - see https://support.atlassian.com/bitbucket-cloud/docs/yaml-anchors/ for how to use anchors to only do so once

Note: If there are source tables you do not have (see Disable models for non-existent sources), you may still include them, as long as you have set the right variables to False.

2. Set the has_defined_sources variable (scoped to the zuora package) to True, like such:

# dbt_project.yml
vars:
  zuora:
    has_defined_sources: true

Disable models for non-existent sources

Your Zuora connection may not sync every table that this package expects. This might be because you are excluding those tables. If you are not using those tables, you can disable the corresponding functionality in the package by specifying the variable in your dbt_project.yml. By default, all packages are assumed to be true. You only have to add variables for tables you want to disable in the following way:

vars: 
  zuora__using_credit_balance_adjustment: false # Disable if you do not have the credit balance adjustment table
  zuora__using_refund: false # Disable if you do not have the refund table
  zuora__using_refund_invoice_payment: false # Disable if you do not have the refund invoice payment table
  zuora__using_taxation_item: false # Disable if you do not have the taxation item table

Configure the multicurrency variable for customers billing in multiple currencies

Zuora allows the functionality for multicurrency to bill customers in various currencies. If you are an account utilizing multicurrency, make sure to set the zuora__using_multicurrency variable to true in dbt_project.yml so the amounts in our data models accurately reflect the home currency values in your native account currency.

vars:
  zuora__using_multicurrency: true #Enable if you are utilizing multicurrency, false by default.

Multicurrency Support Disclaimer (and how you can help)

We were not able to develop the package using the multicurrency variable, so we had to execute our best judgement when building these models. If you encounter any issues with enabling the variable, please file a bug report with us and we can work together to fix any issues you encounter.

(Optional) Additional configurations

Expand to view configurations

Enabling Standardized Billing Model

This package contains the zuora__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. This model is enabled by default. To disable it, set the zuora__standardized_billing_model_enabled variable to false in your dbt_project.yml:

vars:
  zuora__standardized_billing_model_enabled: false # true by default.

Setting the date range for the account daily overview and monthly recurring revenue models

By default, the zuora__account_daily_overview will aggregate data for the entire date range of your data set based on the minimum and maximum invoice_date values from the invoice source table, and zuora__monthly_recurring_revenue based on the service_start_date from the invoice_item source table.

However, you may limit this date range if desired by defining the following variables for each respective model (the zuora_overview_ variables refer to the zuora__account_daily_overview, the zuora_mrr_ variables apply to zuora__monthly_recurring_revenue).

vars:
    zuora_daily_overview_first_date: "yyyy-mm-dd"
    zuora_daily_overview_last_date: "yyyy-mm-dd"

    zuora_mrr_first_date: "yyyy-mm-dd"
    zuora_mrr_last_date: "yyyy-mm-dd"

Passing Through Additional Fields

This package includes all source columns defined in the macros folder of this package. You can add more columns using our pass-through column variables. 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:

vars:
  zuora_account_pass_through_columns: 
    - name: "new_custom_field"
      alias: "custom_field"
      transform_sql: "cast(custom_field as string)"
    - name: "another_one"
  zuora_subscription_pass_through_columns:
    - name: "this_field"
      alias: "cool_field_name"
  zuora_rate_plan_pass_through_columns:
    - name: "another_field"
      alias: "cooler_field_name"
  zuora_rate_plan_charge_pass_through_columns:
    - name: "yet_another_field"
      alias: "coolest_field_name"

Change the build schema

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

models:
    zuora:
      +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.
      staging:
        +schema: my_new_schema_name # Leave +schema: blank to use the default 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:
    zuora_<default_source_table_name>_identifier: your_table_name 

Snowflake Users

If you do not use the default all-caps naming conventions for Snowflake, 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 below identifier in your dbt_project.yml file:

vars:
    zuora_order_identifier: "ORDER" # as an example, must include the double-quotes and correct case

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

Expand to view 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"]

    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

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 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. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.

Opinionated Modelling Decisions

This dbt package takes several opinionated stances in order to provide the customer several options to better understand key subscription metrics. Those include:

• Evaluating a history of billing transactions, examined at either the invoice or invoice item level
• How to calculate monthly recurring revenue and at which grains to assess it, either looking at it granularly at the charge (invoice item) or account monthly level
• Developing a custom churn analysis that you can find in the analysis folder that's built on the account monthly level, but also giving the customer the ability to look at churn from a subscription or rate plan charge level.

If you would like a deeper explanation of the decisions we made to our models in this 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.