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Loading Data from notion to bigquery using Python and dlt Library

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Welcome to this technical documentation on how to use the open-source Python library, dlt, to load data from Notion to BigQuery. Notion is a versatile platform that allows you to think, write, plan, and manage projects in a way that suits your needs. On the other hand, BigQuery is a serverless, cost-effective enterprise data warehouse that can handle data across multiple clouds and scales with your data. This guide will walk you through the process of transferring data from Notion to BigQuery using dlt, making your data management tasks easier and more efficient. For more information about Notion, visit Notion's official guide.

dlt Key Features​

  • Automated Maintenance: dlt offers automated maintenance through schema inference and evolution alerts. With its short, declarative code, maintenance becomes simple. Read more
  • Run Anywhere: dlt can be run wherever Python runs - on Airflow, serverless functions, notebooks, and more. No external APIs, backends or containers are required, and it scales on both micro and large infrastructures. Read more
  • User-Friendly Interface: dlt provides a user-friendly, declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Read more
  • 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
  • Scalability and Fine-tuning: dlt offers several mechanisms and configuration options to scale up and fine-tune pipelines, including running extraction, normalization, and load in parallel, writing sources and resources that are run in parallel via thread pools and async execution, and fine-tuning memory buffers, intermediary file sizes, and compression options. 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 BigQuery:

pip install "dlt[bigquery]"

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

# create a new directory
mkdir notion_pipeline
cd notion_pipeline
# initialize a new pipeline with your source and destination
dlt init notion bigquery
# 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[bigquery]>=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.bigquery]
location = "US"

[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # 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 BigQuery 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='bigquery',
        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 BigQuery 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: You can use Github Actions as a CI/CD runner to deploy your dlt pipeline. It's basically free and you can specify when the GitHub Action should run using a cron schedule expression. Learn more about it here.
  • Deploy with Airflow: Google Composer, a managed Airflow environment provided by Google, can be used to deploy your dlt pipeline. It creates an Airflow DAG for your pipeline script that you can customize. Learn more about it here.
  • Deploy with Google Cloud Functions: Google Cloud Functions is a serverless execution environment for building and connecting cloud services. You can use it to deploy your dlt pipeline. Learn more about it here.
  • Other Deployment Options: There are other ways to deploy your dlt pipeline. You can learn more about them here.

The running in production section will teach you about:​

  • How to Monitor Your Pipeline: dlt provides a detailed guide on how to monitor your pipeline, ensuring that you can keep track of your data loading tasks in real-time.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any issues or changes in your pipeline. This feature helps you to react promptly to any unforeseen events.
  • Set Up Tracing: dlt also allows you to set up tracing for your pipeline. This feature aids in debugging and understanding the behavior of your pipeline, ensuring smooth operation in production.

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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