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Python Data Loading from notion to dremio using dlt Library

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This page provides technical documentation for using the open-source Python library, dlt, to load data from Notion to Dremio. Notion is a versatile platform for capturing thoughts, managing projects, and even running a company. It offers a customizable environment to organize information. Dremio, on the other hand, is a data lakehouse solution providing flexibility, scalability, and performance to meet the needs of leaders at all stages of their data journey. By leveraging dlt, users can effectively extract data from Notion and load it into Dremio for further analysis and insights. For more information about Notion, visit https://www.notion.so/help/guides/what-is-notion.

dlt Key Features​

  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about pipeline metadata, schema enforcement and curation, and schema evolution.
  • Scalability and Performance: dlt provides several mechanism and configuration options to scale up and finetune pipelines. It supports running extraction, normalization and load in parallel, and offers options to finetune the memory buffers, intermediary file sizes and compression options. Read more about performance.
  • Easy Data Extraction: Extracting data with dlt is simple and efficient, thanks to its use of iterators, chunking, and parallelization techniques. It also utilizes implicit extraction DAGs that allow efficient API calls for data enrichments or transformations. Learn more about how to build a pipeline.
  • Automated and User-friendly: dlt simplifies maintenance with schema inference and evolution, alerts, and short declarative code. It runs wherever Python runs and offers a user-friendly, declarative interface. Read more about how dlt works.
  • Supportive Community: dlt is a constantly growing library with a supportive community. You can join the Slack community to discuss recent releases or what you can build with dlt. You can also check out the code on GitHub and make feature requests 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 Dremio:

pip install "dlt[dremio]"

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

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

[destination.dremio.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 = 32010

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 Dremio 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='dremio',
        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 Dremio 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 can be deployed using Github Actions. This provides a CI/CD runner that you can use for free.

  • Deploy with Airflow: You can use Airflow to deploy dlt. Airflow is a platform to programmatically author, schedule and monitor workflows.

  • Deploy with Google Cloud Functions: dlt can be deployed using Google Cloud Functions. This allows you to run your code without provisioning or managing servers.

  • Other Deployment Options: There are several other ways to deploy dlt. You can find more information on these methods here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides useful features to monitor your pipeline's performance and status. This includes tracking the state of your pipeline, inspecting load packages, and examining job statuses. Learn more about how to monitor your pipeline here.
  • Set Up Alerts: Stay on top of your pipeline's performance and potential issues with dlt's alerting capabilities. You can set up alerts based on various criteria to ensure you're immediately notified of any issues. Find out more about setting up alerts here.
  • Set Up Tracing: dlt allows you to set up tracing to gain insights into your pipeline's execution. This can help you identify bottlenecks and optimize your pipeline for better performance. Learn more 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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