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Python Data Loading from notion to redshift using dlt Library

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This page provides technical documentation on how to load data from Notion, a versatile platform for thought capture, project management, and company operations, into Redshift, Amazon's cloud-based, petabyte-scale data warehouse service. The process leverages the open-source Python library dlt, which simplifies the extraction and loading of data. For more information on Notion, please refer to their official guide here. By using dlt, you can efficiently transfer your Notion data into Redshift for robust data analysis and management.

dlt Key Features​

  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more about it here.
  • Scalability and Finetuning: dlt offers several mechanism and configuration options to scale up and finetune pipelines. It leverages iterators, chunking, and parallelization techniques for efficient processing of large datasets. Learn more about this here.
  • Amazon Redshift Integration: dlt provides detailed instructions for setting up Amazon Redshift as a destination. It covers everything from installation, Redshift cluster setup, to adding credentials. Check out the guide here.
  • Data Extraction with dlt: dlt simplifies data extraction by allowing you to decorate your data-producing functions with loading or incremental extraction metadata. It also incorporates the concept of implicit extraction DAGs to handle the dependencies between data sources and their transformations automatically. Read more about this here.
  • Community Support: dlt has a vibrant community that supports many features and use cases. You can join their Slack to find recent releases or discuss what you can build with dlt. Join the community 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 Redshift:

pip install "dlt[redshift]"

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

# create a new directory
mkdir notion_pipeline
cd notion_pipeline
# initialize a new pipeline with your source and destination
dlt init notion redshift
# 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[redshift]>=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.redshift.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!
port = 5439
connect_timeout = 15

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 Redshift 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='redshift',
        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 Redshift 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 you can use for free. You need to specify when the GitHub Action should run using a cron schedule expression. Find out more about how to deploy using Github Actions here.
  • Airflow: dlt supports deployment through Airflow, a platform to programmatically author, schedule and monitor workflows. This method creates an Airflow DAG for your pipeline script that you should customize. Learn more about deploying with Airflow here.
  • Google Cloud Functions: With dlt, you can deploy your data pipelines using Google Cloud Functions, a serverless execution environment for building and connecting cloud services. Find out more about deploying with Google Cloud Functions here.
  • More Options: There are other ways to deploy dlt as well. Check out other deployment options here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides a comprehensive monitoring system to keep track of your pipeline's performance and status. This allows you to identify and rectify any issues promptly, ensuring your pipeline runs smoothly and efficiently. You can learn more about it here.
  • Set Up Alerts: To keep you informed about the health and status of your pipeline, dlt allows you to set up alerts. These alerts can notify you about any errors, failures, or important events that occur within your pipeline. To learn how to set up alerts, click here.
  • Set Up Tracing: Tracing is a powerful tool that allows you to track the execution of your pipeline and identify any bottlenecks or performance issues. dlt provides a simple way to set up tracing for your pipeline. For more information on how to set up tracing, visit this link.

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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