Python Data Loading from notion
to snowflake
using dlt
Library
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This page provides technical documentation on using the open-source Python library, dlt
, to load data from Notion
to Snowflake
. Notion
is a versatile platform that allows for the capture and organization of thoughts, project management, and even company operations. On the other hand, Snowflake
is a cloud-based data warehousing solution designed for the storage, processing, and analysis of large data volumes. The process involves using dlt
to extract data from Notion
and load it into Snowflake
for further analysis and manipulation. For more information about Notion
, visit https://www.notion.so/help/guides/what-is-notion.
dlt
Key Features
- Snowflake Installation:
dlt
offers a seamless integration with Snowflake, a popular data warehousing service. You can install the DLT library with Snowflake dependencies usingpip install dlt[snowflake]
. Learn more - Robust Governance Support:
dlt
pipelines provide robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. This contributes to better data management practices, compliance adherence, and overall data governance. Learn more - Scalability and Fine-tuning:
dlt
offers several mechanisms and configuration options to scale up and fine-tune pipelines. This includes running extraction, normalization, and load in parallel, and fine-tuning memory buffers, intermediary file sizes, and compression options. Learn more - Extensive Authentication Types: Snowflake destination in
dlt
accepts three authentication types - password authentication, key pair authentication, and external authentication. This flexibility allows for secure and convenient data access. Learn more - Schema Management:
dlt
provides a comprehensive schema management system. The schema describes the structure of normalized data and provides instructions on how the data should be processed and loaded. Users can provide hints to influence how tables, columns, and other metadata are generated and how the data is loaded. 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 Snowflake
:
pip install "dlt[snowflake]"
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 Snowflake
. You can run the following commands to create a starting point for loading data from Notion
to Snowflake
:
# create a new directory
mkdir notion_pipeline
cd notion_pipeline
# initialize a new pipeline with your source and destination
dlt init notion snowflake
# 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[snowflake]>=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.snowflake.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!
warehouse = "warehouse" # please set me up!
role = "role" # please set me up!
2.1. Adjust the generated code to your usecase
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='snowflake',
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 Snowflake
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
allows you to deploy your pipeline using Github Actions, a CI/CD runner that you can use for free. - Deploy with Airflow: You can deploy your pipeline using Airflow, a managed environment provided by Google Composer.
- Deploy with Google Cloud Functions:
dlt
also supports deployment with Google Cloud Functions, a serverless execution environment for building and connecting cloud services. - Other Deployment Options: For more information on other deployment options, you can visit the deployment page on the official
dlt
documentation.
The running in production section will teach you about:
- Monitor Your Pipeline:
dlt
provides tools to monitor your pipeline, giving you insights into the performance and health of your data loads. You can find more information on how to monitor your pipeline here. - Set Up Alerts: With
dlt
, you can set up alerts to notify you of any issues or changes in your pipeline. This feature allows you to respond quickly to any problems, ensuring your data loads run smoothly. Learn more about setting up alerts here. - Set Up Tracing: Tracing allows you to track the execution of your pipeline, helping you identify any bottlenecks or issues.
dlt
makes it easy to set up tracing for your pipeline. Check out the guide on how to set up tracing here.
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