Stripe dbt Package

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

Resources

Number of materialized models¹: 61
Connector documentation
- Stripe connector documentation
- Stripe ERD
dbt package documentation

What does this dbt package do?

This package enables you to better understand your Stripe transactions, enhance balance transaction entries with useful fields, and generate metrics tables for account activity analysis. It creates enriched models with metrics focused on transaction analysis, customer insights, and revenue tracking.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_stripe

Final output tables

By default, this package materializes the following final tables:

Table	Description
stripe__balance_transactions	Represents each change to your Stripe balance with transaction context. Example Analytics Questions: What types of transactions are most impacting my Stripe balance? How much did fees, refunds, or disputes reduce net revenue this quarter?
stripe__invoice_details	Contains invoice records with associated charge, customer, and subscription data. Example Analytics Questions: What is the average invoice value by customer segment? Which customers have the highest outstanding invoices?
stripe__invoice_line_item_details	Includes line items with charge, customer, subscription, and pricing details. Example Analytics Questions: Which products or services contribute most to total invoiced revenue? Are there any products consistently discounted or refunded?
stripe__daily_overview	Summarizes daily and rolling Stripe transaction totals by type. Example Analytics Questions: What is the trend in daily net payments and refunds? What is the MRR trend over the last 6 months?
stripe__subscription_details	Contains subscription records with customer and payment metrics. Example Analytics Questions: How many active subscriptions are there by plan or product? What is the average customer subscription length before cancellation?
stripe__customer_overview	Shows customer-level metrics with transaction details and associations. Example Analytics Questions: Who are the top 10 customers by total lifetime value? How many customers made a payment in the last 90 days?
stripe__activity_itemized_2	Lists balance transactions with invoice, fee, refund, and customer data. Example Analytics Questions: What are the exact transaction-level fees for each invoice or customer? How much are we paying in interchange and platform fees per transaction?
stripe__balance_change_from_activity_itemized_3	Reconciles Stripe balance changes like a detailed bank statement. Example Analytics Questions: What was the source of each Stripe balance change over the last month? How accurate is my accounting ledger compared to Stripe's balance records?
stripe__ending_balance_reconciliation_itemized_4	Matches bank payouts with unsettled Stripe transactions. Example Analytics Questions: Which transactions remain unsettled as of the last payout? Do all automatic payouts reconcile fully with balance changes?
stripe__payout_itemized_3	Details expected and actual payout amounts and statuses. Example Analytics Questions: When should I expect my next payout, and for how much? Are there any delayed or failed payouts that need follow-up?
stripe__line_item_enhanced	Provides unified reporting across billing platforms on product, customer, and revenue metrics. See the Fivetran Billing Model Streamlit App for more details. Example Analytics Questions: What are the top revenue-generating products or SKUs? What is the average revenue per user (ARPU) by subscription plan?
stripe__subscription_item_mrr_report	Shows both contracted and billed MRR (monthly recurring revenue) with discounts applied at the subscription item level. Tracks MRR changes over time, classifying each month as new, expansion, contraction, churned, reactivation, or unchanged. Example Analytics Questions: What percentage of subscription customers are churning each month as compared to new? How much subscription revenue was lost last year due to discounts?

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 Stripe connection syncing data into your destination.
A BigQuery, Snowflake, Redshift, Databricks, or PostgreSQL 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.