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Load Data from notion to postgresql using Python dlt Library

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This page provides technical documentation on how to load data from Notion to PostgreSQL using the open-source Python library, dlt. Notion is a flexible workspace that allows you to capture thoughts, manage projects, and more, in a way that suits your needs. PostgreSQL is a powerful, open-source object-relational database system, capable of handling complex data workloads. With dlt, you can effectively transfer data from Notion to PostgreSQL, leveraging the strengths of both platforms. For more information about Notion, visit here.

dlt Key Features​

  • Robust 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
  • Postgres Integration: dlt provides seamless integration with PostgreSQL. It provides a detailed setup guide, supports all write dispositions, and supports multiple file formats. Read more
  • Data Types: dlt supports a wide range of data types, including text, double, bool, timestamp, date, time, bigint, binary, complex, decimal, and wei. Read more
  • Tables and Columns: dlt allows users to define the key components of a schema, such as tables and columns. It provides detailed information about table and column schemas, including variant columns. Read more
  • DuckDB Integration: dlt supports DuckDB as a destination. It provides a detailed setup guide, supports all write dispositions, and supports multiple file formats. 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 PostgreSQL:

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

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

dlt[postgres]>=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.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 Notion source in our docs.
  • Read more about setting up the PostgreSQL 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='postgres',
        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 PostgreSQL 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 is a CI/CD runner that is free for use. You can specify when the Github Action should run using a cron schedule expression. Learn more about it here.
  • Deploy with Airflow: dlt also supports deployment with Airflow. It creates an Airflow DAG for your pipeline script that you should customize. The DAG uses dlt Airflow wrapper to make this process trivial. Learn more about it here.
  • Deploy with Google Cloud Functions: You can also deploy your pipeline using Google Cloud Functions. This is a serverless execution environment for building and connecting cloud services. Learn more about it here.
  • Other Deployment Options: dlt supports a variety of other deployment options. You can explore them here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt allows you to keep a close eye on your pipeline's performance and health. Find out more about how to monitor your pipeline here.
  • Set Up Alerts: Stay informed about any issues or changes in your pipeline. dlt provides a simple way to set up alerts. Learn more about setting up alerts here.
  • Set Up Tracing: Tracing is a powerful feature that allows you to track your data's journey through the pipeline. Discover how to set up tracing with dlt 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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