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Loading Data from Notion to Azure Cosmos DB with dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to Azure Cosmos DB. You can get the connection string for your Azure Cosmos DB database as described in the Azure Cosmos DB Docs.

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This documentation provides a guide on how to load data from Notion to Azure Cosmos DB using the open-source Python library dlt. Notion is a versatile tool where you can think, write, and plan, allowing you to capture thoughts, manage projects, or even run an entire company in a customizable space. Azure Cosmos DB is a fully managed NoSQL and relational database designed for modern app development, offering a free trial to get started. By leveraging dlt, you can seamlessly transfer data from Notion to Azure Cosmos DB, ensuring efficient data management and integration. For more details about Notion, visit this guide.

dlt Key Features​

  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, including load IDs for incremental transformations and data vaulting. Learn more
  • Schema Enforcement and Curation: Ensure data consistency and quality by enforcing and curating schemas, which define the structure of normalized data. Learn more
  • Schema Evolution Alerts: dlt proactively alerts users to schema changes, allowing stakeholders to review, validate, and update downstream processes as needed. Learn more
  • Scaling and Finetuning: Offers several mechanisms and configuration options to scale up and finetune pipelines, such as parallel processing and memory buffer adjustments. Learn more
  • DuckDB Integration: Easily integrate with DuckDB, a fast and easy-to-use database, with support for various file formats and naming conventions. 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 Azure Cosmos DB:

pip install "dlt[postgres]"

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

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

[destination.postgres.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 = 5432
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 Azure Cosmos DB 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='postgres',
        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 Azure Cosmos DB 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 use GitHub Actions to deploy your dlt pipelines with ease. Follow the guide here.
  • Deploy with Airflow: Utilize Airflow and Google Composer for deploying dlt pipelines. Detailed instructions can be found here.
  • Deploy with Google Cloud Functions: Discover how to deploy dlt pipelines using Google Cloud Functions by following the steps here.
  • Explore other deployment options: Check out additional methods for deploying dlt pipelines here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipeline in production to ensure it runs smoothly and efficiently. How to Monitor your pipeline
  • Set up alerts: Discover how to set up alerts for your dlt pipeline to stay informed about its status and any potential issues. Set up alerts
  • And set up tracing: Implement tracing in your dlt pipeline to gain detailed insights into its execution and performance. 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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