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Load Stripe Data to Local Filesystem Using dlt in Python

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Stripe is a comprehensive payments platform that helps businesses scale quickly with its robust payment processing capabilities. Supporting over 135 currencies, Stripe offers simple APIs, easy integration, and transparent pricing. This documentation will guide you on how to load data from Stripe to The Local Filesystem using the open-source python library dlt. The Local Filesystem destination allows you to store data in a local folder, making it easy to create data lakes. You can store data in formats such as JSONL, Parquet, or CSV. For detailed information about Stripe, visit this link.

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.
  • Scalable Data Extraction: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. This allows for efficient processing of large datasets. Learn more at Scalability via iterators, chunking, and parallelization.
  • Implicit Extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle dependencies between data sources and their transformations automatically. This ensures data consistency and integrity. Read more about it at Implicit extraction DAGs.
  • Secure Credential Management: Store sensitive information securely in the secrets.toml file. This ensures that access tokens and other credentials are kept safe. Learn more at General Usage: Credentials.
  • Multiple Authentication Types for Snowflake: Snowflake destination accepts three authentication types: password authentication, key pair authentication, and external authentication. Detailed setup can be found at Snowflake Authentication Types.

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 The Local Filesystem:

pip install "dlt[filesystem]"

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

# 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 filesystem
# 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[filesystem]>=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.filesystem]
dataset_name = "dataset_name" # please set me up!
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_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 The Local Filesystem destination in our docs.

The default filesystem destination is configured to connect to AWS S3. To load to a local directory, remove the [destination.filesystem.credentials] section from your secrets.toml and provide a local filepath as the bucket_url.

[destination.filesystem] # in ./dlt/secrets.toml
bucket_url="file://path/to/my/output"

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

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='filesystem',
        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='filesystem',
        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='filesystem',
        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 The Local Filesystem 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: Learn how to deploy your dlt pipeline using GitHub Actions.
  • Deploy with Airflow: Follow this guide to deploy your pipeline with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Explore how to deploy your dlt pipeline using Google Cloud Functions.
  • Other Deployment Methods: Discover additional ways to deploy your dlt pipeline here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipelines in production to ensure smooth and reliable operation. How to Monitor your pipeline
  • Set up alerts: Set up alerts to notify you of any issues or anomalies in your dlt pipeline, ensuring quick response times and minimal downtime. Set up alerts
  • Set up tracing: Implement tracing to gain detailed insights into the execution of your dlt pipelines, helping you diagnose and resolve issues more efficiently. And set up tracing

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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