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Using dlt to Load Data from Notion to Azure Synapse in Python

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Welcome to our technical documentation about using the open-source Python library, dlt, to load data from Notion to Azure Synapse. Notion is a comprehensive workspace that allows you to capture thoughts, manage projects, or even run an entire company, all tailored to your preferences. On the other hand, Azure Synapse is a limitless analytics service that combines enterprise data warehousing and Big Data analytics. By utilizing dlt, you can easily bridge these two platforms, transferring your data from Notion to Azure Synapse for advanced analytics. For more information about Notion, please visit Notion Guides.

dlt Key Features​

  • Installation with Synapse Dependencies: The dlt library can be installed with Synapse dependencies using the command pip install dlt[synapse]. More details can be found here.
  • Governance Support: dlt pipelines offer robust governance support through key mechanisms like pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Learn more about these features here.
  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines. More information can be found here.
  • Staging Support: Synapse supports Azure Blob Storage as a file staging destination. dlt first uploads Parquet files to the blob container, and then instructs Synapse to read the Parquet file and load its data into a Synapse table. More details can be found here.
  • Data Extraction: Extracting data with dlt is simple and scalable. It leverages iterators, chunking, and parallelization techniques for efficient processing of large datasets. More information can be found 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 Azure Synapse:

pip install "dlt[synapse]"

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

# create a new directory
mkdir notion_pipeline
cd notion_pipeline
# initialize a new pipeline with your source and destination
dlt init notion synapse
# 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[synapse]>=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.synapse]
create_indexes = false
default_table_index_type = "heap"
staging_use_msi = false

[destination.synapse.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 = 1433
connect_timeout = 15
driver = "driver" # 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 Azure Synapse 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='synapse',
        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 Synapse 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 supports deployment through Github Actions, allowing you to automate your pipelines and schedule them to run at specified intervals. Learn more about this process here.
  • Deploy with Airflow: You can also deploy your dlt pipelines using Airflow, a popular tool for managing complex computational workflows and data processing pipelines. Find out how to deploy with Airflow here.
  • Deploy with Google Cloud Functions: For those using Google Cloud, dlt can be deployed using Google Cloud Functions. This allows you to run your pipelines in a fully managed environment. Learn more about this deployment method here.
  • Other Deployment Methods: dlt supports a variety of other deployment methods to suit your specific needs. Check out the full list of deployment options here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides various ways to monitor your pipeline, ensuring that your data loads are successful and efficient. Learn more about monitoring your pipeline here.
  • Set Up Alerts: With dlt, you can set up alerts to be notified of any issues or changes in your pipeline. This allows you to respond quickly to any problems and keep your pipeline running smoothly. Learn how to set up alerts here.
  • Set Up Tracing: Tracing in dlt allows you to track the execution of your pipeline, providing valuable insights into its performance and helping you identify areas for improvement. Learn how to set 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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