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Loading Data from Notion to AlloyDB with dlt in Python


We will be using the dlt PostgreSQL destination to connect to AlloyDB. You can get the connection string for AlloyDB from the GCP AlloyDB Console.

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This page provides technical documentation for loading data from Notion to AlloyDB using the open-source Python library dlt. Notion is a versatile platform where you can think, write, and plan, allowing you to capture thoughts, manage projects, or run an entire company in a way that suits you. AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service designed for demanding workloads, including hybrid transactional and analytical processing. It combines a Google-built database engine with a cloud-based, multi-node architecture to offer enterprise-grade performance, reliability, and availability. This guide will walk you through the process of using dlt to seamlessly transfer your data from Notion to AlloyDB. For further information about Notion, visit this guide.

dlt Key Features

  • Pipeline Metadata: dlt pipelines leverage metadata to provide governance capabilities, including load IDs for tracking data loads and facilitating data lineage and traceability. Read more about lineage.
  • Schema Enforcement and Curation: dlt empowers users to enforce and curate schemas, ensuring data consistency and quality. By adhering to predefined schemas, pipelines maintain data integrity and facilitate standardized data handling practices. Read more: Adjust a schema docs.
  • Schema Evolution: dlt enables proactive governance by alerting users to schema changes, allowing stakeholders to review and validate changes, update downstream processes, or perform impact analysis.
  • Scalability via Iterators, Chunking, and Parallelization: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques, allowing efficient processing of large datasets. Read more about performance.
  • Implicit Extraction DAGs: dlt incorporates the concept of implicit extraction DAGs to handle dependencies between data sources and their transformations automatically, ensuring data consistency and integrity. Read more about building a pipeline.

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 AlloyDB:

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

# 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:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── notion/ # folder with source specific files
│ └── ...
├── # 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

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
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

api_key = "api_key" # please set me up!

dataset_name = "dataset_name" # please set me up!

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 AlloyDB 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, 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(

data = notion_databases()

info =

if __name__ == "__main__":

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:


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 AlloyDB 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 automate your pipeline deployment using GitHub Actions. Read more
  • Deploy with Airflow: Use Airflow and Google Composer to manage and deploy your pipelines. Read more
  • Deploy with Google Cloud Functions: Discover how to use Google Cloud Functions for deploying your pipelines. Read more
  • Explore Other Deployment Options: Find various other methods and platforms to deploy your pipeline. Read more

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 smooth operation and quick issue detection. How to Monitor your pipeline
  • Set up alerts: Configure alerts to get notified of any issues or important events in your dlt pipeline, ensuring you can take timely action. Set up alerts
  • Set up tracing: Implement tracing to gain insights into the execution of your dlt pipeline, helping you to debug and optimize 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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