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Loading Stripe Data to Timescale with dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to Timescale. You can get the connection string for your timescale database as described in the Timescale Docs.

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Stripe is a complete payments platform that helps businesses scale faster with its secure payment processing capabilities. Supporting over 135 currencies, Stripe offers simple APIs, easy integration, and transparent pricing. To load data from Stripe to Timescale, an open-source Python library called dlt can be used. Timescale is engineered to handle demanding workloads, including time series, vector, events, and analytics data. Built on PostgreSQL, Timescale offers expert support at no extra charge. Using dlt, you can efficiently extract and load your Stripe data into Timescale for advanced analytics and reporting. For more details, visit Stripe's website.

dlt Key Features​

  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, including load IDs for tracking data loads and facilitating data lineage and traceability. Read more about lineage.
  • Schema Enforcement and Curation: Ensure data consistency and quality by defining schemas that guide the processing and loading of data. Read more about schema adjustments.
  • Scalability via iterators, chunking, and parallelization: Efficient processing of large datasets by breaking them down into manageable chunks and leveraging parallel processing capabilities. Learn more about performance.
  • Implicit extraction DAGs: Automatically handle dependencies between data sources and their transformations, ensuring data consistency and integrity. Build a pipeline tutorial.
  • Matomo Integration: Load detailed analytics summaries and individual user sessions from Matomo using the dlt verified source. Learn more about Matomo integration.

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 Timescale:

pip install "dlt[postgres]"

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

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

[destination.postgres.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 = 5432
connect_timeout = 15

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 Timescale 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='postgres',
        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='postgres',
        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='postgres',
        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 Timescale 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: Use GitHub Actions to automate your pipeline deployment. Learn how to set it up here.
  • Deploy with Airflow: Utilize Airflow and Google Composer for managed workflow orchestration. Follow the guide here.
  • Deploy with Google Cloud Functions: Discover how to deploy your pipeline using Google Cloud Functions here.
  • Explore other deployment options: Check out various other methods to deploy your pipeline here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure smooth operations and quick issue resolution. How to Monitor your pipeline
  • Set up alerts: Set up alerts to get notified of any issues or anomalies in your dlt pipeline, allowing for proactive management and quick response. Set up alerts
  • Set up tracing: Implement tracing to keep track of your dlt pipeline's performance and debug any issues that arise during execution. 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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