Recurly dbt Package
This dbt package transforms data from Fivetran's Recurly connector into analytics-ready tables.
Resources
- Number of materialized models¹: 47
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to enhance balance transaction entries with useful fields, create customized analysis tables to examine churn and monthly recurring revenue, and generate metrics tables for account activity analysis. It creates enriched models with metrics focused on transactions, subscriptions, and customer behavior.
Output schema
Final output tables are generated in the following target schema:
<your_database>.<connector/schema_name>_recurly
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| recurly__account_daily_overview | Provides daily account snapshots with transaction counts, invoices, charges, credits, discounts, taxes, and rolling balance totals to track account financial health and payment activity evolution over time. Example Analytics Questions:
|
| recurly__account_overview | Consolidates account profiles with comprehensive transaction metrics including total invoices, charges, credits, balances, discounts, taxes, and monthly totals to understand account financial performance and relationships. Example Analytics Questions:
|
| recurly__balance_transactions | Chronicles individual balance transactions including charges, credits, discounts, taxes, and refunds by type and state with product details to provide granular visibility into invoice components and balance calculations. Example Analytics Questions:
|
| recurly__churn_analysis | Analyzes subscription churn with activation dates, cancellation dates, expiration info, subscription states, churn reasons, and period details to identify retention risks and understand cancellation drivers. Example Analytics Questions:
|
| recurly__monthly_recurring_revenue | Tracks monthly recurring revenue (MRR) by account and MRR type (new, expansion, contraction, churn) comparing current month MRR to previous month MRR to measure subscription business health and revenue trends. Example Analytics Questions:
|
| recurly__subscription_overview | Provides detailed subscription profiles with activation dates, cancellation dates, expiration info, subscription states, billing periods, renewal settings, pricing, and trial details to monitor subscription lifecycle and financial contribution. Example Analytics Questions:
|
| recurly__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 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. Visit the app for more details. |
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 Recurly 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.
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.