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Loading Data from notion to AWS S3 using dlt in Python

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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This page provides technical documentation for using the open-source Python library, dlt, to load data from Notion to AWS S3. Notion is a versatile tool for capturing thoughts, managing projects, or running a company. AWS S3 is a remote file system and bucket storage solution that can also serve as a staging area for other destinations or as a foundation for a data lake. With dlt, you can easily transfer data from Notion to AWS S3. For more information on Notion, visit https://www.notion.so/help/guides/what-is-notion.

dlt Key Features​

  • Robust Governance Support: dlt pipelines offer robust governance support through mechanisms like pipeline metadata utilization, schema enforcement and curation, and schema change alerts. More details can be found here.
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. Read more about adjusting a schema here.
  • Scaling and Finetuning: dlt provides several mechanisms and configuration options to scale up and fine-tune pipelines. Learn more about performance here.
  • Filesystem & Buckets: dlt can store data in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. More on this can be found here.
  • Advanced Usage with Branches, Local Folders, or Git Repos: dlt allows deployment from a branch of the verified-sources repo or another repo. More information on this can be found here.

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 AWS S3:

pip install "dlt[filesystem]"

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 AWS S3. You can run the following commands to create a starting point for loading data from Notion to AWS S3:

# create a new directory
mkdir my-notion-pipeline
cd my-notion-pipeline
# initialize a new pipeline with your source and destination
dlt init notion filesystem
# 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[filesystem]>=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.filesystem]
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # please set me up!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 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='filesystem',
        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 AWS S3 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​

  • GitHub Actions: dlt can be deployed using GitHub Actions. This is a CI/CD runner that can be used for free. You can specify when the GitHub Action should run using a cron schedule expression.
  • Airflow: You can deploy dlt using Airflow. Google Composer, a managed Airflow environment provided by Google, can be used for this purpose.
  • Google Cloud Functions: dlt can also be deployed using Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code.
  • Other Deployment Options: There are other ways to deploy dlt, providing flexibility to choose the method that best suits your needs.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides various tools to monitor your pipeline, ensuring that it runs smoothly and efficiently. You can inspect and save load info and trace, and alert on schema changes. Learn more about it here.
  • Set up alerts: dlt allows you to set up alerts to notify you of any issues or anomalies in your pipeline. This ensures that you are always aware of the status of your pipeline and can take immediate action when necessary. Learn how to set up alerts here.
  • Set up tracing: Tracing is a crucial aspect of running a pipeline in production. dlt makes it easy to set up tracing, allowing you to track the execution of your pipeline and identify any potential bottlenecks or issues. Learn 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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