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Loading Data from notion to Microsoft SQL Server with dlt in Python

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This page provides technical documentation about using the open-source Python library dlt to load data from Notion to Microsoft SQL Server. Notion is a versatile platform that allows you to think, write, and plan in one space. It's an ideal tool for capturing thoughts, managing projects, or even running a company. On the other hand, Microsoft SQL Server is a relational database management system (RDBMS) that allows applications and tools to connect and communicate using Transact-SQL. This guide will help you leverage dlt to bridge these two powerful tools. For more detailed information about Notion, visit Notion's official guide.

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 governance support here.
  • Microsoft SQL Server Support: dlt supports Microsoft SQL Server as a destination. It provides detailed instructions for setting up a pipeline, loading data, and handling data types. Check out the guide here.
  • Data Types: dlt supports a variety of data types including text, double, bool, timestamp, date, time, bigint, binary, complex, decimal, and wei. Find out more about these data types here.
  • Tracing: dlt provides identifiers, data lineage, and schema lineage for tracing. This helps in maintaining data integrity and tracking changes. Read more about tracing here.
  • DuckDB Support: dlt supports DuckDB as a destination. It provides a comprehensive guide for setting up a pipeline, loading data, and handling data types. Check out the guide 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 Microsoft SQL Server:

pip install "dlt[mssql]"

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

# create a new directory
mkdir notion_pipeline
cd notion_pipeline
# initialize a new pipeline with your source and destination
dlt init notion mssql
# 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[mssql]>=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.mssql.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 Microsoft SQL Server 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='mssql',
        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 Microsoft SQL Server 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 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. More details can be found here.
  • Deploy with Airflow: dlt can also be deployed with Airflow. This is a platform to programmatically author, schedule and monitor workflows. You can create an Airflow DAG for your pipeline script that you should customize. More information can be found here.
  • Deploy with Google Cloud Functions: Another way to deploy dlt is by using Google Cloud Functions. This is a serverless execution environment for building and connecting cloud services. More details can be found here.
  • Other Deployment Options: Apart from the above, there are other ways to deploy dlt. You can explore more options here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: With dlt, you can easily keep an eye on your pipeline's performance and progress. It provides detailed insights into the pipeline's operation, helping you to identify any potential issues or bottlenecks. Learn more about it here.
  • Set Up Alerts: dlt allows you to set up alerts to keep you informed about your pipeline's status. This feature is particularly useful in production environments where timely notifications of any issues can help prevent larger problems. Learn how to set up alerts here.
  • Set Up Tracing: Tracing is a powerful feature in dlt that allows you to track the execution of your pipeline. It can help you understand the flow of data and identify any potential issues in your pipeline. 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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