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Python Guide: Loading Data from Notion to AWS S3 using dlt Library

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This page provides technical documentation on how to use the open-source Python library dlt to load data from Notion to AWS S3. Notion is a versatile platform that allows you to capture thoughts, manage projects, or run an entire company. On the other hand, AWS S3 is a filesystem destination that facilitates the creation of data lakes, supporting data upload in JSONL, Parquet, or CSV formats. By leveraging the capabilities of dlt, you can streamline the process of transferring data from Notion to AWS S3. For more details about Notion, visit here.

dlt Key Features​

  • Robust Governance Support: dlt pipelines provide strong governance capabilities through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more about these features in the documentation.
  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines, including running extraction, normalization, and load in parallel. Learn more about performance.
  • Provider Key Formats: dlt supports different formats for keys in TOML or Environment Variables. It also provides Environment and TOML providers for storing sensitive information and configuration values. Check out the config providers documentation for more details.
  • Bucket Storage and Credentials Setup: dlt supports various storage options like AWS S3, Google Storage, Azure Blob Storage, and local file system. Detailed setup guides for these storage options can be found here.
  • Advanced Initialization Options: dlt offers advanced options for initializing projects with branches, local folders, or git repos. More information on this can be found in the add a verified source walkthrough.

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 notion_pipeline
cd 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:

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.filesystem]
dataset_name = "dataset_name" # please set me up!
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!

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 AWS S3 destination in our docs.

By default, the filesystem destination will store your files as JSONL. You can tell your pipeline to choose a different format with the loader_file_format property that you can set directly on the pipeline or via your config.toml. Available values are jsonl, parquet and csv:

[pipeline] # in ./dlt/config.toml
loader_file_format="parquet"

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​

  • Deploy with Github Actions: Learn how to deploy a pipeline using Github Actions.
  • Deploy with Airflow: Follow this guide to deploy a pipeline with Airflow and Google Composer.
  • Deploy with Google Cloud Functions: Discover how to deploy a pipeline with Google Cloud Functions.
  • Other Deployment Methods: Explore other options for deploying pipelines here.

The running in production section will teach you about:​

  • Monitor your pipeline: Learn how to effectively monitor your dlt pipeline to ensure smooth operation and quick identification of issues. How to Monitor your pipeline
  • Set up alerts: Configure alerts to get notified about important events and potential issues in your dlt pipeline. Set up alerts
  • Set up tracing: Implement tracing to gain insights into the performance and execution details of your dlt pipeline. And set up tracing

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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