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Loading Stripe Data to MS SQL Server with Python's dlt Library

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This page provides technical documentation on leveraging dlt, an open-source Python library, to load data from Stripe into Microsoft SQL Server. Stripe, a comprehensive payment processing platform, offers easy integration, transparent pricing, and supports over 135 currencies. Microsoft SQL Server is a relational database management system (RDBMS) that facilitates communication via Transact-SQL. By using dlt, you can efficiently transfer data from Stripe to Microsoft SQL Server, streamlining your business processes. For more details on Stripe, visit https://stripe.com.

dlt Key Features​

  • Google Analytics: dlt offers a verified source for Google Analytics. This source can load data using Google Analytics API to the destination of your choice, providing insights about user engagement with your website or application.

  • Governance Support: dlt pipelines provide robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance.

  • Microsoft SQL Server: dlt supports Microsoft SQL Server as a destination. This includes the ability to create unique indexes, sync dlt state, and handle different data types.

  • Data Extraction: Extracting data with dlt is efficient and scalable. This is achieved through the use of iterators, chunking, parallelization, and the utilization of implicit extraction DAGs that allow efficient API calls for data enrichments or transformations.

  • Next Steps: After getting started with dlt, there are numerous resources and tutorials to help you take full advantage of the library. These include choosing your destinations, checking verified sources, using SQL or Pandas for data access, and 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 Microsoft SQL Server:

pip install "dlt[mssql]"

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

# 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 mssql
# 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[mssql]>=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.mssql.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 = 1433
connect_timeout = 15
driver = "driver" # 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 Microsoft SQL Server 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, 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='mssql',
        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='mssql',
        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='mssql',
        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 Microsoft SQL Server 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 an easy way to deploy your pipeline using Github Actions. It prepares your pipeline for deployment and gives you step by step instruction on how to accomplish it.
  • Deploy with Airflow: You can also deploy your pipeline using Airflow. dlt generates an Airflow DAG for your pipeline script that you can easily customize.
  • Deploy with Google Cloud Functions: dlt allows you to deploy your pipeline using Google Cloud Functions. This method is ideal for serverless deployment.
  • Other Deployment Methods: Besides the above methods, dlt supports various other deployment methods which you can explore here.

The running in production section will teach you about:​

  • Monitor your pipeline: It's crucial to keep an eye on your pipeline's performance and status to ensure it's functioning as expected. dlt provides a comprehensive guide on how to monitor your pipeline effectively. Check it out here.
  • Set up alerts: To stay informed about any changes or issues in your pipeline, setting up alerts is a must. dlt allows you to set up alerts easily, helping you react promptly to any potential problems. Learn more about it here.
  • Set up tracing: Tracing is a powerful tool for debugging and understanding your pipeline's behavior. dlt offers an easy way to set up tracing for your pipeline. Find out how to do it 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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