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Loading Data from Notion to EDB BigAnimal with dlt in Python

tip

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

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Loading data from Notion to EDB BigAnimal can streamline your workflow by centralizing information from a versatile workspace to a robust, fully managed database service. Notion allows you to capture thoughts, manage projects, and run entire operations in one place. EDB BigAnimal offers a fully managed database-as-a-service, making it simple to set up, manage, and scale your databases using PostgreSQL or EDB Postgres Advanced Server. By using the open-source Python library dlt, you can efficiently transfer data from Notion to EDB BigAnimal, ensuring data consistency and ease of management. For more information about Notion, visit here.

dlt Key Features​

  • Pipeline Metadata Utilization: dlt pipelines leverage metadata, including load IDs, to provide governance capabilities, enabling incremental transformations and data lineage. Learn more
  • Schema Enforcement and Curation: Ensure data consistency and quality by defining the structure of normalized data with schemas that guide processing and loading. Learn more
  • Schema Evolution Alerts: Get notified of schema changes in the source data to take necessary actions like reviewing changes or updating downstream processes. Learn more
  • Scalability via Iterators, Chunking, and Parallelization: Efficiently process large datasets by breaking them down into manageable chunks and processing them in parallel. Learn more
  • Implicit Extraction DAGs: Automatically handle dependencies between data sources and their transformations, ensuring data consistency and integrity. 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 EDB BigAnimal:

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

# 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 EDB BigAnimal 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 EDB BigAnimal 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 set up and deploy your dlt pipeline using GitHub Actions for CI/CD automation. Detailed instructions can be found here.
  • Deploy with Airflow and Google Composer: Follow this guide to deploy your dlt pipeline using Airflow managed by Google Composer. The step-by-step process is available here.
  • Deploy with Google Cloud Functions: This guide explains how to deploy your dlt pipeline using Google Cloud Functions for serverless execution. Find the instructions here.
  • More deployment options: Explore additional methods to deploy your dlt pipeline, including various cloud services and orchestration tools. Discover more 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 everything runs smoothly. How to Monitor your pipeline
  • Set up alerts: Set up alerts to get notified when something goes wrong with your dlt pipeline. Set up alerts
  • Set up tracing: Implement tracing to keep track of your pipeline's performance and troubleshoot issues effectively. 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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