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Loading Data from stripe to dremio using Python's dlt Library

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This page provides technical documentation on how to load data from Stripe, a comprehensive payment processing platform, to Dremio, a scalable data lakehouse solution, using the open-source Python library, dlt. Stripe offers easy integration with over 135 currencies, simple APIs, and transparent pricing, making it an ideal platform for scaling businesses. On the other hand, Dremio brings users closer to their data, offering flexibility, scalability, and performance. The dlt library serves as a bridge, facilitating the efficient transfer of data from Stripe to Dremio. For more information about Stripe, visit https://stripe.com.

dlt Key Features​

  • Automated maintenance: With schema inference and evolution and alerts, and with short declarative code, maintenance becomes simple. Learn more about it here.
  • Runs anywhere Python runs: dlt can run on Airflow, serverless functions, notebooks. No external APIs, backends or containers, scales on micro and large infra alike. Learn more about it here.
  • User-friendly, declarative interface: dlt removes knowledge obstacles for beginners while empowering senior professionals. Learn more about it here.
  • Robust Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about it here.
  • Scalability and Finetuning: dlt offers several mechanism and configuration options to scale up and finetune pipelines. Learn more about it here.

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 Dremio:

pip install "dlt[dremio]"

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 Dremio. You can run the following commands to create a starting point for loading data from Stripe to Dremio:

# 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 dremio
# 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:


pandas>=2.0.0
stripe>=5.0.0
dlt[dremio]>=0.3.5

You now have the following folder structure in your project:

stripe_analytics_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── stripe_analytics/                   # folder with source specific files
│   └── ...
├── stripe_analytics_pipeline.py        # 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

[runtime]
log_level="WARNING"  # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see https://dlthub.com/docs/telemetry
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

[sources.stripe_analytics]
stripe_secret_key = "stripe_secret_key" # please set me up!

[destination.dremio]
dataset_name = "dataset_name" # please set me up!
staging_data_source = "staging_data_source" # please set me up!

[destination.dremio.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
username = "username" # please set me up!
host = "host" # please set me up!
port = 32010

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 Dremio 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 stripe_analytics_pipeline.py, 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
from stripe_analytics import (
    ENDPOINTS,
    INCREMENTAL_ENDPOINTS,
    incremental_stripe_source,
    metrics_resource,
    stripe_source,
)


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.

    Args:
        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(
        pipeline_name="stripe_analytics",
        destination='dremio',
        dataset_name="stripe_updated",
    )
    source = stripe_source(
        endpoints=endpoints, start_date=start_date, end_date=end_date
    )
    load_info = pipeline.run(source)
    print(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.

    Args:
        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(
        pipeline_name="stripe_analytics",
        destination='dremio',
        dataset_name="stripe_incremental",
    )
    # load all data on the first run that created before end_date
    source = incremental_stripe_source(
        endpoints=endpoints,
        initial_start_date=initial_start_date,
        end_date=end_date,
    )
    load_info = pipeline.run(source)
    print(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 = pipeline.run(source)
    # 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 = pipeline.run(source)
    # 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(
        pipeline_name="stripe_analytics",
        destination='dremio',
        dataset_name="stripe_metrics",
    )

    # 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
    source_event.resources["Event"].apply_hints(
        columns={
            "created": {"data_type": "timestamp"},
        }
    )

    source_subs.resources["Subscription"].apply_hints(
        columns={
            "created": {"data_type": "timestamp"},
        }
    )

    load_info = pipeline.run(data=[source_subs, source_event])
    print(load_info)

    resource = metrics_resource()
    load_info = pipeline.run(resource)
    print(load_info)


if __name__ == "__main__":
    load_data()
    # # load only data that was created during the period between the Jan 1, 2024 (incl.), and the Feb 1, 2024 (not incl.).
    # from pendulum import datetime
    # 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.).
    # load_incremental_endpoints(
    #     endpoints=("Event",),
    #     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
    # load_data_and_get_metrics()

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

python stripe_analytics_pipeline.py

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 Dremio 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 allows you to deploy your pipeline using Github Actions. This CI/CD runner is basically free and you can specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: You can also deploy your pipeline with Airflow. dlt will create an Airflow DAG for your pipeline script that you can customize. This option is particularly useful for Google Composer users.
  • Deploy with Google Cloud Functions: dlt supports deployment with Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with your code.
  • Explore other deployment options: Besides the above, dlt provides other deployment options to suit your specific needs. Check out the documentation for more information.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides features to monitor your pipeline in production. You can track the progress of your pipeline, inspect and save load information, and even inspect, save, and alert on schema changes. To learn more, visit the How to Monitor your pipeline guide.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any issues or changes in your pipeline. This feature is crucial for maintaining the health of your pipeline in a production environment. Check out the Set up alerts guide for more information.
  • Set Up Tracing: Tracing is an essential part of running a pipeline in production. dlt allows you to get a runtime trace from the pipeline that contains timing information on extract, normalize, and load steps. To learn how to set up tracing, read the And set up tracing guide.

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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