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Loading Data from notion to databricks using Python's dlt Library

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This page provides technical documentation on how to load data from Notion to Databricks using the open-source Python library, dlt. Notion is a versatile workspace that allows users to think, write, and plan in a single space, facilitating various tasks from capturing thoughts to running an entire company. On the other hand, Databricks is a unified data analytics platform, developed by the original creators of Apache Spark™, that promotes innovation by combining data science, engineering, and business. The dlt library serves as the bridge between these two platforms, enabling seamless data transfer and manipulation. For more information about Notion, you can visit their help page.

dlt Key Features​

  • Automated maintenance: With schema inference and evolution, and alerts, and short declarative code, maintenance becomes simple. Learn more
  • Run it anywhere: dlt can be run on Airflow, serverless functions, notebooks. No external APIs, backends or containers, scales on micro and large infra alike. Learn more
  • User-friendly, declarative interface: dlt provides a user-friendly, declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Learn more
  • Scalability and extraction efficiency: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. It also utilizes implicit extraction DAGs that allow efficient API calls for data enrichments or transformations. Learn more
  • Robust governance support: dlt pipelines provide robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. 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 Databricks:

pip install "dlt[databricks]"

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

# create a new directory
mkdir notion_pipeline
cd notion_pipeline
# initialize a new pipeline with your source and destination
dlt init notion databricks
# 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[databricks]>=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.databricks.credentials]
catalog = "catalog" # please set me up!
server_hostname = "server_hostname" # please set me up!
http_path = "http_path" # please set me up!
access_token = "access_token" # 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 Databricks 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='databricks',
        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 Databricks 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: Github Actions is a CI/CD runner that you can use for free. You can schedule when the Github Action should run using a cron schedule expression. Here's the guide on how to deploy a pipeline with Github Actions.
  • Deploy with Airflow and Google Composer: Google Composer is a managed Airflow environment provided by Google. It creates an Airflow DAG for your pipeline script that you should customize. Here's the guide on how to deploy a pipeline with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Google Cloud Functions is a serverless execution environment for building and connecting cloud services. Here's the guide on how to deploy a pipeline with Google Cloud Functions.
  • More Deployment Options: There are several other ways to deploy your pipelines. Check out the other deployment options to find the one that suits your needs.

The running in production section will teach you about:​

  • Monitor Your Pipeline: The How to Monitor your pipeline guide provides detailed instructions on how to keep an eye on your pipeline's performance and status. It covers various monitoring techniques that can help you identify and resolve issues promptly.
  • Set Up Alerts: The Set up alerts guide explains how to configure alerting for your pipeline. This ensures that you are promptly notified of any issues or changes that may affect your pipeline's operation.
  • Implement Tracing: The Set up tracing guide offers comprehensive instructions on how to implement tracing in your pipeline. Tracing allows you to track the execution of your pipeline and provides valuable insights for debugging and performance optimization.

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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