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Loading Data from notion to aws athena using Python's dlt Library

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This technical documentation provides guidance on using the open-source Python library, dlt, to load data from Notion to AWS Athena. Notion is a comprehensive tool that allows you to think, write, plan, and even manage entire projects or businesses. AWS Athena is an interactive query service that simplifies data analysis in Amazon S3 using standard SQL, and our implementation supports iceberg tables. The integration between Notion and AWS Athena via dlt opens up new possibilities for data management and analysis. For more details on Notion, visit Notion Guide.

dlt Key Features​

  • Governance Support in dlt Pipelines: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more about it here.

  • AWS Athena / Glue Catalog: The athena destination stores data as parquet files in s3 buckets and creates external tables in aws athena. You can then query those tables with athena sql commands which will then scan the whole folder of parquet files and return the results. Learn more here.

  • Extracting data with dlt: Extracting data with dlt is simple - you simply decorate your data-producing functions with loading or incremental extraction metadata, which enables dlt to extract and load by your custom logic. Read more about it here.

  • Data loading: Data loading with dlt happens by storing parquet files in an s3 bucket and defining a schema on athena. dlt internal tables are saved as Iceberg tables. Learn more about it here.

  • Asana Verified Source: Asana is a widely used web-based project management and collaboration tool that helps teams stay organized, focused, and productive. With Asana, team members can easily create, assign, and track tasks, set deadlines, and communicate with each other in real-time. Learn more about it 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 Athena:

pip install "dlt[athena]"

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

# create a new directory
mkdir notion_pipeline
cd notion_pipeline
# initialize a new pipeline with your source and destination
dlt init notion athena
# 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[athena]>=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.athena]
query_result_bucket = "query_result_bucket" # please set me up!
athena_work_group = "athena_work_group" # please set me up!

[destination.athena.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 Athena 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='athena',
        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 Athena 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 provides a simple way to deploy your pipelines using Github Actions. You can schedule the actions and automate your pipeline runs with ease.
  • Deploy with Airflow: If you are using Airflow for managing your data pipelines, dlt integrates seamlessly. Learn how to deploy a pipeline with Airflow and Google Composer from this guide.
  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions. Check out this tutorial to learn more.
  • Other Deployment Options: dlt offers a variety of other deployment options to suit your specific needs. Check out the deployment documentation for more information.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides a comprehensive monitoring system for your pipeline. You can keep track of the pipeline's progress, inspect load packages, and monitor schema changes. Learn more about how to monitor your pipeline here.
  • Set Up Alerts: With dlt, you can set up alerts to notify you about important events or issues in your pipeline. This feature can help you respond quickly to any problems and ensure that your pipeline runs smoothly. Find more about setting up alerts here.
  • Set Up Tracing: dlt also allows you to set up tracing for your pipeline. This feature provides detailed insights into the pipeline's execution, which can be useful for debugging and optimization. You can find more information about setting 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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