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Python Data Loading from Notion to MotherDuck with the dlt Library

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This page provides technical documentation on using the open-source Python library dlt to load data from Notion into MotherDuck. Notion is an all-in-one workspace where you can write, plan, and manage projects, while MotherDuck, based on DuckDB, is a fast in-process analytical database with a feature-rich SQL dialect. This guide will help you leverage the power of dlt to efficiently transfer data from Notion to MotherDuck. For more information about Notion, visit Notion's Guide.

dlt Key Features​

  • MotherDuck Installation: The DLT library comes with MotherDuck dependencies, which can be easily installed using the pip command. Learn more
  • Data Loading: By default, parquet files and COPY command is used to move files to remote duckdb database. INSERT format is also supported and will execute large INSERT queries directly into the remote database. Learn more
  • dbt Support: This destination integrates with dbt via dbt-duckdb which is a community supported package. dbt version >= 1.5 is required. Learn more
  • State Syncing: This destination fully supports dlt state sync which helps in keeping your data up to date. Learn more
  • Automated Tests: Each destination must pass few hundred automatic tests. MotherDuck is passing those tests, ensuring the quality and reliability of the service. Learn 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 MotherDuck:

pip install "dlt[motherduck]"

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

# create a new directory
mkdir notion_pipeline
cd notion_pipeline
# initialize a new pipeline with your source and destination
dlt init notion motherduck
# 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:

dlt[motherduck]>=0.3.5

You now have the following folder structure in your project:

notion_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── notion/                   # folder with source specific files
│   └── ...
├── notion_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.notion]
api_key = "api_key" # please set me up!

[destination.motherduck.credentials]
database = "database" # please set me up!
password = "password" # 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 Notion source in our docs.
  • Read more about setting up the MotherDuck 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 notion_pipeline.py, as well as a folder notion 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:


import dlt

from notion import notion_databases


def load_databases() -> None:
    """Loads all databases from a Notion workspace which have been shared with
    an integration.
    """
    pipeline = dlt.pipeline(
        pipeline_name="notion",
        destination='motherduck',
        dataset_name="notion_data",
    )

    data = notion_databases()

    info = pipeline.run(data)
    print(info)


if __name__ == "__main__":
    load_databases()

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

python notion_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline notion info

You can also use streamlit to inspect the contents of your MotherDuck destination for this:

# install streamlit
pip install streamlit
# run the streamlit app for your pipeline with the dlt cli:
dlt pipeline notion 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: With dlt, you can easily prepare your pipeline for deployment using Github Actions. This CI/CD runner can be set up to run based on a cron schedule expression. Find out more about deploying with Github Actions here.
  • Deploy with Airflow and Google Composer: dlt also supports deployment with Airflow and Google Composer. This managed Airflow environment provided by Google can be used to create an Airflow DAG for your pipeline script. Learn more about deploying with Airflow and Google Composer here.
  • Deploy with Google Cloud Functions: Another deployment option is using Google Cloud Functions. dlt can help you set up a serverless function that runs your pipeline in response to cloud events. Get more information on deploying with Google Cloud Functions here.
  • Explore Other Deployment Options: dlt offers a range of deployment options to cater to different needs and preferences. You can explore other ways to deploy your pipeline here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to easily monitor your pipeline, ensuring that your data loads are running smoothly and efficiently. You can find more information on how to do this here.
  • Set Up Alerts: Stay ahead of any potential issues by setting up alerts. dlt makes it simple to set up alerts and notifications, so you can be notified of any issues as soon as they arise. Check out this guide to learn more.
  • Set Up Tracing: Tracing is a powerful tool that allows you to track the execution of your pipeline. dlt makes it easy to set up tracing, helping you to identify and resolve any issues quickly. Learn more about setting up tracing here.

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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