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Loading Stripe Data to AWS S3 with Python's dlt Library

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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This page provides technical documentation on how to load data from stripe, a comprehensive payment processing platform, to aws s3, a remote file system and bucket storage, using an open-source Python library known as dlt. stripe offers simple APIs, easy integration, and transparent pricing across 135+ currencies, making it an ideal platform for scaling businesses. On the other hand, aws s3 is primarily used as a staging area for other destinations or for quickly building a data lake. The dlt library simplifies this process, making it easy for developers to transfer data between these two platforms. For more information about stripe, visit https://stripe.com.

dlt Key Features​

  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read More
  • Scalability and Performance: dlt offers several mechanisms and configuration options to scale up and fine-tune pipelines. These include running extraction, normalization and load in parallel, and fine-tuning the memory buffers, intermediary file sizes and compression options. Read More
  • Data Extraction: Extracting data with dlt is simple and efficient. It offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. It also utilizes implicit extraction DAGs that allow efficient API calls for data enrichments or transformations. Read More
  • Filesystem & Buckets: Filesystem destination in dlt stores data in remote file systems and bucket storages like S3, google storage or azure blob storage. It is primarily used as a staging for other destinations, but can also be used to quickly build a data lake. Read More
  • Advanced Deployment Options: dlt allows deploying from a branch of a repo or from another repo. This provides flexibility and customization options for your data pipelines. Read 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 AWS S3:

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 AWS S3. You can run the following commands to create a starting point for loading data from Stripe to AWS S3:

# create a new directory
mkdir my-stripe_analytics-pipeline
cd my-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:

my-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:

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

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]
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!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Stripe source in the dlt verifed sources documentation.

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, 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 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.).
    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 AWS S3 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 can be easily deployed using Github Actions. This involves scheduling the action using a cron schedule expression. Learn more about this process here.
  • Deploy with Airflow: dlt can also be deployed using Airflow. This involves creating an Airflow DAG for your pipeline script. You can find more detailed instructions here.
  • Deploy with Google Cloud Functions: Another option is to deploy dlt using Google Cloud Functions. This involves creating a Google Cloud Function that triggers your pipeline. Learn more about it here.
  • Other Deployment Options: There are several other ways to deploy dlt. You can find more options and detailed instructions here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides robust monitoring features that let you keep track of your data pipeline's performance and status. This includes viewing the load info and trace, inspecting and saving schema changes, and more. Check out the guide on How to Monitor your pipeline.
  • Set Up Alerts: Stay informed about your pipeline's status with dlt's alerting features. You can set up alerts for various events in your pipeline, ensuring that you are always up-to-date with any changes or issues. Learn how to Set up alerts.
  • Implement Tracing: dlt allows you to implement tracing in your data pipeline. This feature provides you with detailed information about the execution of your pipeline, which can be invaluable for debugging and optimization. Find out more about how to 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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