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Loading Stripe Payment Data into DuckDB using Python's dlt Library

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Welcome to our technical guide on using dlt, an open-source Python library, to load data from Stripe into DuckDB. Stripe is a comprehensive payments platform that supports over 135 currencies, offers simple APIs, easy integration and transparent pricing to help businesses scale faster. On the other hand, DuckDB is a speedy in-process analytical database that supports a feature-rich SQL dialect along with deep integrations into client APIs. This guide will walk you through how to use dlt to efficiently transfer data from Stripe to DuckDB. More information about Stripe can be found at

dlt Key Features

  • Easy to get started: dlt is a Python library that is easy to use and understand. It is designed to be simple to use and easy to understand. Type pip install dlt and you are ready to go. Learn more
  • Support for various data sources: dlt supports a wide range of data sources, including APIs, databases, and file systems. This versatility makes it a powerful tool for data extraction and loading. Learn more
  • Comprehensive tutorial: dlt provides a detailed tutorial that guides you through the process of building a data pipeline. The tutorial covers a variety of topics, such as fetching data from APIs, managing data loading behaviors, and handling secrets securely. Learn more
  • Support for various destinations: dlt supports a wide range of destinations, including DuckDB, BigQuery, and PostgreSQL. This flexibility allows you to choose the destination that best suits your data processing needs. Learn more
  • Built-in data exploration: dlt integrates with Streamlit to provide a built-in data exploration feature. This feature allows you to quickly and easily inspect the data loaded into your destination. Learn more

Getting started with your pipeline locally

0. Prerequisites

dlt requires Python 3.8 or higher. Additionally, you need to have the pip package manager installed, and we recommend using a virtual environment to manage your dependencies. You can learn more about preparing your computer for dlt in our installation reference.

1. Install dlt

First you need to install the dlt library with the correct extras for DuckDB:

pip install "dlt[duckdb]"

The dlt cli has a useful command to get you started with any combination of source and destination. For this example, we want to load data from Stripe to DuckDB. You can run the following commands to create a starting point for loading data from Stripe to DuckDB:

# create a new directory
mkdir stripe_analytics_pipeline
cd stripe_analytics_pipeline
# initialize a new pipeline with your source and destination
dlt init stripe_analytics duckdb
# install the required dependencies
pip install -r requirements.txt

The last command will install the required dependencies for your pipeline. The dependencies are listed in the requirements.txt:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── stripe_analytics/ # folder with source specific files
│ └── ...
├── # your main pipeline script
├── requirements.txt # dependencies for your pipeline
└── .gitignore # ignore files for git (not required)

2. Configuring your source and destination credentials

The dlt cli will have created a .dlt directory in your project folder. This directory contains a config.toml file and a secrets.toml file that you can use to configure your pipeline. The automatically created version of these files look like this:

generated config.toml

# put your configuration values here

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
dlthub_telemetry = true

generated secrets.toml

# put your secret values and credentials here. do not share this file and do not push it to github

stripe_secret_key = "stripe_secret_key" # please set me up!

2.1. Adjust the generated code to your usecase

Further help setting up your source and destinations
  • Read more about setting up the Stripe source in our docs.
  • Read more about setting up the DuckDB destination in our docs.

3. Running your pipeline for the first time

The dlt cli has also created a main pipeline script for you at, as well as a folder stripe_analytics that contains additional python files for your source. These files are your local copies which you can modify to fit your needs. In some cases you may find that you only need to do small changes to your pipelines or add some configurations, in other cases these files can serve as a working starting point for your code, but will need to be adjusted to do what you need them to do.

The main pipeline script will look something like this:

from typing import Optional, Tuple

import dlt
from pendulum import DateTime, datetime
from stripe_analytics import (

def load_data(
endpoints: Tuple[str, ...] = ENDPOINTS + INCREMENTAL_ENDPOINTS,
start_date: Optional[DateTime] = None,
end_date: Optional[DateTime] = None,
) -> None:
This demo script uses the resources with non-incremental
loading based on "replace" mode to load all data from provided endpoints.

endpoints: A tuple of endpoint names to retrieve data from. Defaults to most popular Stripe API endpoints.
start_date: An optional start date to limit the data retrieved. Defaults to None.
end_date: An optional end date to limit the data retrieved. Defaults to None.
pipeline = dlt.pipeline(
source = stripe_source(
endpoints=endpoints, start_date=start_date, end_date=end_date
load_info =

def load_incremental_endpoints(
endpoints: Tuple[str, ...] = INCREMENTAL_ENDPOINTS,
initial_start_date: Optional[DateTime] = None,
end_date: Optional[DateTime] = None,
) -> None:
This demo script demonstrates the use of resources with incremental loading, based on the "append" mode.
This approach enables us to load all the data
for the first time and only retrieve the newest data later,
without duplicating and downloading a massive amount of data.

Make sure you're loading objects that don't change over time.

endpoints: A tuple of incremental endpoint names to retrieve data from.
Defaults to Stripe API endpoints with uneditable data.
initial_start_date: An optional parameter that specifies the initial value for dlt.sources.incremental.
If parameter is not None, then load only data that were created after initial_start_date on the first run.
Defaults to None. Format: datetime(YYYY, MM, DD).
end_date: An optional end date to limit the data retrieved.
Defaults to None. Format: datetime(YYYY, MM, DD).
pipeline = dlt.pipeline(
# load all data on the first run that created before end_date
source = incremental_stripe_source(
load_info =

# # load nothing, because incremental loading and end date limit
# source = incremental_stripe_source(
# endpoints=endpoints,
# initial_start_date=initial_start_date,
# end_date=end_date,
# )
# load_info =
# print(load_info)
# # load only the new data that created after end_date
# source = incremental_stripe_source(
# endpoints=endpoints,
# initial_start_date=initial_start_date,
# )
# load_info =
# print(load_info)

def load_data_and_get_metrics() -> None:
With the pipeline, you can calculate the most important metrics
and store them in a database as a resource.
Store metrics, get calculated metrics from the database, build dashboards.

Supported metrics:
Monthly Recurring Revenue (MRR),
Subscription churn rate.

Pipeline returns both metrics.

Use Subscription and Event endpoints to calculate the metrics.

pipeline = dlt.pipeline(

# Event is an endpoint with uneditable data, so we can use 'incremental_stripe_source'.
source_event = incremental_stripe_source(endpoints=("Event",))
# Subscription is an endpoint with editable data, use stripe_source.
source_subs = stripe_source(endpoints=("Subscription",))

# convert dates to the timestamp format
"created": {"data_type": "timestamp"},

"created": {"data_type": "timestamp"},

load_info =[source_subs, source_event])

resource = metrics_resource()
load_info =

if __name__ == "__main__":
# load only data that was created during the period between the Jan 1, 2024 (incl.), and the Feb 1, 2024 (not incl.).
load_data(start_date=datetime(2024, 1, 1), end_date=datetime(2024, 2, 1))
# load only data that was created during the period between the May 3, 2023 (incl.), and the March 1, 2024 (not incl.).
initial_start_date=datetime(2023, 5, 3),
end_date=datetime(2024, 3, 1),
# load Subscription and Event data, calculate metrics, store them in a database

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:


4. Inspecting your load result

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline stripe_analytics info

You can also use streamlit to inspect the contents of your DuckDB destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline stripe_analytics show

5. Next steps to get your pipeline running in production

One of the beauties of dlt is, that we are just a plain Python library, so you can run your pipeline in any environment that supports Python >= 3.8. We have a couple of helpers and guides in our docs to get you there:

The Deploy section will show you how to deploy your pipeline to

  • Deploy with GitHub Actions: dlt provides a convenient way to deploy your pipelines using GitHub Actions. This approach uses the CI/CD runner provided by GitHub, which you can use for free.
  • Deploy with Airflow: You can also deploy your dlt pipelines using Airflow. This method uses Google Composer, a managed Airflow environment provided by Google, to create an Airflow DAG for your pipeline script.
  • Deploy with Google Cloud Functions: dlt also supports deployment using Google Cloud Functions. This serverless execution environment runs your code in response to events without requiring you to manage any server infrastructure.
  • Other Deployment Methods: For other deployment methods, you can visit the deploy a pipeline section of the dlt documentation.

The running in production section will teach you about:

  • Monitor Your Pipeline: dlt provides a suite of monitoring tools that help you keep track of your data pipeline's performance. It provides detailed information about the data that has been loaded, the status of the pipeline, and any errors that may have occurred. You can learn more about how to monitor your pipeline here.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any issues with your data pipeline. This allows you to promptly address any problems and ensure that your pipeline is running smoothly. Check out the guide on how to set up alerts here.
  • Set Up Tracing: Tracing is another powerful feature provided by dlt that helps you understand the execution of your data pipeline. It provides detailed information about the execution of each step in your pipeline, which can help you identify bottlenecks and optimize your pipeline. Learn more about how to set up tracing here.

Available Sources and Resources

For this verified source the following sources and resources are available

Source incremental_stripe_source

This source provides detailed transactional and subscription data from Stripe's payment platform.

Resource NameWrite DispositionDescription
EventappendThis resource retrieves significant activities in a Stripe account. It includes detailed information about various transactions like payments, invoices, subscriptions, etc.

Source stripe_source

"Stripe source provides transactional data, subscription details, and key business metrics from Stripe platform."

Resource NameWrite DispositionDescription
MetricsappendThis resource provides key metrics for the Stripe account, such as churn rate, creation date, and monthly recurring revenue (MRR).
SubscriptionreplaceThis resource includes detailed information about subscriptions in the Stripe account, including billing details, discount coupons, invoice settings, and more.

Additional pipeline guides

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!


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