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Python Data Loading from notion to snowflake using dlt Library

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This page provides technical documentation on using the open-source Python library, dlt, to load data from Notion to Snowflake. Notion is a versatile platform that allows for the capture and organization of thoughts, project management, and even company operations. On the other hand, Snowflake is a cloud-based data warehousing solution designed for the storage, processing, and analysis of large data volumes. The process involves using dlt to extract data from Notion and load it into Snowflake for further analysis and manipulation. For more information about Notion, visit https://www.notion.so/help/guides/what-is-notion.

dlt Key Features​

  • Snowflake Installation: dlt offers a seamless integration with Snowflake, a popular data warehousing service. You can install the DLT library with Snowflake dependencies using pip install dlt[snowflake]. Learn more
  • Robust Governance Support: dlt pipelines provide robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. This contributes to better data management practices, compliance adherence, and overall data governance. Learn more
  • Scalability and Fine-tuning: dlt offers several mechanisms and configuration options to scale up and fine-tune pipelines. This includes running extraction, normalization, and load in parallel, and fine-tuning memory buffers, intermediary file sizes, and compression options. Learn more
  • Extensive Authentication Types: Snowflake destination in dlt accepts three authentication types - password authentication, key pair authentication, and external authentication. This flexibility allows for secure and convenient data access. Learn more
  • Schema Management: dlt provides a comprehensive schema management system. The schema describes the structure of normalized data and provides instructions on how the data should be processed and loaded. Users can provide hints to influence how tables, columns, and other metadata are generated and how the data is loaded. 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 Snowflake:

pip install "dlt[snowflake]"

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

# create a new directory
mkdir my-notion-pipeline
cd my-notion-pipeline
# initialize a new pipeline with your source and destination
dlt init notion snowflake
# 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[snowflake]>=0.3.5

You now have the following folder structure in your project:

my-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:

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

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.snowflake.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!
warehouse = "warehouse" # please set me up!
role = "role" # please set me up!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the Snowflake destination in the dlt destinations documentation.

Likewise you can find the setup instructions for Notion source in the dlt verifed sources documentation.

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='snowflake',
        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 Snowflake 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 allows you to deploy your pipeline using Github Actions, a CI/CD runner that you can use for free.
  • Deploy with Airflow: You can deploy your pipeline using Airflow, a managed environment provided by Google Composer.
  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
  • Other Deployment Options: For more information on other deployment options, you can visit the deployment page on the official dlt documentation.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides tools to monitor your pipeline, giving you insights into the performance and health of your data loads. You can find more information on how to monitor your pipeline here.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any issues or changes in your pipeline. This feature allows you to respond quickly to any problems, ensuring your data loads run smoothly. Learn more about setting up alerts here.
  • Set Up Tracing: Tracing allows you to track the execution of your pipeline, helping you identify any bottlenecks or issues. dlt makes it easy to set up tracing for your pipeline. Check out the guide on how to set 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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