Zuora dbt package (Docs)

📣 What does this dbt package do?

• Produces modeled tables that leverage Zuora data from Fivetran's connector in the format described by this ERD and build off the output of our Zuora source package.

• Enables you to better understand your Zuora data. The package achieves this by performing the following:

  • Enhance the balance transaction entries with useful fields from related tables.
  • Create customized analysis tables to examine churn by subscriptions.
  • Developed a look at gross, net and discount monthly recurring revenue by account.
  • Generate a metrics tables allow you to better understand your account activity over time or at a customer level. These time-based metrics are available on a daily level.

• Generates a comprehensive data dictionary of your source and modeled Zuora data through the dbt docs site.

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

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

model	description
zuora__account_daily_overview	Each record is a day in an account and its accumulated balance totals based on all line item transactions up to that day.
zuora__account_overview	Each record represents an account, enriched with metrics about their associated transactions.
zuora__billing_history	Each record represents an invoice and its various transaction details.
zuora__line_item_history	Each record represents a specific invoice item and its various transaction details.
zuora__monthly_recurring_revenue	Each record represents an account and MRR generated on a monthly basis.
zuora__subscription_overview	Each record represents a subscription, enriched with metrics about time, revenue, state, and period.

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

analysis model	description
zuora__account_churn_analysis	Each record represents an account and whether it has churned in that month or not.

🎯 How do I use the dbt package?

Step 1: Prerequisites

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

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

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']

Step 2: Install the package

Include the following zuora_source 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: [">=0.1.0", "<0.2.0"] # we recommend using ranges to capture non-breaking changes automatically

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

Step 3: Define database and schema variables

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_database: your_destination_name
    zuora_schema: your_schema_name 

Step 4: Disable models for non-existent sources

Your Zuora connector may not be syncing all tabes that this package references. 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, like so:

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

Step 5: Configure the multicurrency variable for customers billing in multiple currencies.

Zuora allows the functionality for multicurrency to bill to 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: false #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 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) Step 6: Additional configurations

Expand to view configurations

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 the dbt_zuora_source package. You can add more columns using our pass-through column variables. 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:

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> + _stg_zuora), 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 blank for just the target_schema
    intermediate:
      +schema: my_new_schema_name # leave blank for just the target_schema
  zuora_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:
    zuora_<default_source_table_name>_identifier: your_table_name 

(Optional) Step 7: 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. Please be aware that 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/zuora_source
      version: [">=0.1.0", "<0.2.0"]

    - 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 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!

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, please refer to 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.
• Submit any questions you have about our packages in our Fivetran dbt community so our Engineering team can provide guidance as quickly as possible!