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Load Data from notion to duckdb in Python using dlt Library

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This guide provides technical documentation on using the open-source Python library, dlt, to load data from Notion to DuckDB. Notion is a versatile platform for capturing thoughts, managing projects, and even running a company. On the other hand, DuckDB is a high-speed in-process analytical database with a feature-rich SQL dialect and deep integrations into client APIs. By leveraging the capabilities of dlt, users can efficiently transfer data between these two platforms. For more information about Notion, please visit Notion's official guide.

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. Read more
  • Support for various destinations: dlt supports numerous destinations including DuckDB and MotherDuck. It provides detailed setup guides for each destination. Read more
  • Robust data pipeline tutorial: dlt offers a comprehensive tutorial on building a data pipeline, covering topics like fetching data from APIs, handling secrets, and making reusable data sources. Read more
  • Governance Support in Pipelines: dlt pipelines offer robust governance support through mechanisms like pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more
  • Integration with dbt and state sync support: The MotherDuck destination integrates with dbt and fully supports dlt state sync, providing powerful data transformation capabilities. 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 DuckDB:

pip install "dlt[duckdb]"

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

# create a new directory
mkdir notion_pipeline
cd notion_pipeline
# initialize a new pipeline with your source and destination
dlt init notion duckdb
# 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[duckdb]>=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!

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 DuckDB 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='duckdb',
        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 DuckDB 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: dlt provides step by step instructions on how to deploy your pipeline using Github Actions. The guide includes how to schedule your deployments and use additional flags for customization.
  • Deploy with Airflow: You can deploy your dlt pipeline using Airflow, a workflow management platform. The guide provides instructions on how to set up Airflow and deploy your pipeline.
  • Deploy with Google Cloud Functions: dlt also supports deployment on Google Cloud Platform. The Google cloud functions guide covers how to deploy your pipeline using Google Cloud Functions.
  • Other Deployment Options: In addition to the methods listed above, dlt supports a variety of other deployment options. Check out the deployment guide for a comprehensive list and detailed instructions.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to easily monitor your data pipeline, ensuring that you can keep track of all the data that is being processed. Learn how to set up monitoring for your pipeline with this guide.
  • Set Up Alerts: Stay informed about the status of your pipeline with dlt's alerting feature. This allows you to receive notifications about any issues that may arise during the execution of your pipeline. Check out this guide to learn how to set up alerts.
  • Enable Tracing: dlt also offers a tracing feature that provides detailed information about the execution of your pipeline. This can be particularly useful for debugging purposes. Learn how to set up tracing with this guide.

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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