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Python Data Loading from github to snowflake with dlt Library

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Welcome to the technical guide on how to use dlt, an open-source Python library, to load data from GitHub into Snowflake. This documentation will show you how to utilize a verified source from GitHub, enabling you to load data related to issues, pull requests, or events from any GitHub repository. This data can then be transferred to your preferred destination in Snowflake, a cloud-based data warehousing platform designed for storing, processing, and analyzing large volumes of data. For further details on the GitHub source, please visit GitHub Documentation.

dlt Key Features​

  • Snowflake Integration: The dlt library provides seamless integration with Snowflake, offering different authentication types for secure data transfer. You can learn more about this feature here.
  • GitHub Verified Source: dlt provides a verified source for the GitHub API, allowing users to load data from any GitHub repository onto a chosen destination. Find more details here.
  • Branches, Local Folders, or Git Repos: With dlt, you can deploy from a branch of the verified-sources repo, from a local folder, or from another git repo. This feature provides flexibility and customization for your data pipelines. Learn more here.
  • Comprehensive Tutorial: dlt offers a detailed tutorial that guides users through the process of building a data pipeline. The tutorial covers various topics, from fetching data from an API to securely handling secrets. Check out the tutorial here.
  • Governance Support: dlt pipelines provide robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices, compliance adherence, and overall data governance. Detailed 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 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 GitHub to Snowflake. You can run the following commands to create a starting point for loading data from GitHub to Snowflake:

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github 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]>=0.3.25

You now have the following folder structure in your project:

github_pipeline/
├── .dlt/
│   ├── config.toml          # configs for your pipeline
│   └── secrets.toml         # secrets for your pipeline
├── github/                   # folder with source specific files
│   └── ...
├── github_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.github]
access_token = "access_token" # 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​

Further help setting up your source and destinations
  • Read more about setting up the GitHub source in our docs.
  • Read more about setting up the Snowflake 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 github_pipeline.py, as well as a folder github 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 github import github_reactions, github_repo_events


def load_duckdb_repo_reactions_issues_only() -> None:
    """Loads issues, their comments and reactions for duckdb"""
    pipeline = dlt.pipeline(
        "github_reactions",
        destination='snowflake',
        dataset_name="duckdb_issues",
        full_refresh=True,
    )
    # get only 100 items (for issues and pull request)
    data = github_reactions(
        "duckdb", "duckdb", items_per_page=100, max_items=100
    ).with_resources("issues")
    print(pipeline.run(data))


def load_airflow_events() -> None:
    """Loads airflow events. Shows incremental loading. Forces anonymous access token"""
    pipeline = dlt.pipeline(
        "github_events", destination='snowflake', dataset_name="airflow_events"
    )
    data = github_repo_events("apache", "airflow", access_token="")
    print(pipeline.run(data))
    # if you uncomment this, it does not load the same events again
    # data = github_repo_events("apache", "airflow", access_token="")
    # print(pipeline.run(data))


def load_dlthub_dlt_all_data() -> None:
    """Loads all issues, pull requests and comments for dlthub dlt repo"""
    pipeline = dlt.pipeline(
        "github_reactions",
        destination='snowflake',
        dataset_name="dlthub_reactions",
        full_refresh=True,
    )
    data = github_reactions("dlt-hub", "dlt")
    print(pipeline.run(data))


if __name__ == "__main__":
    load_duckdb_repo_reactions_issues_only()
    load_airflow_events()
    load_dlthub_dlt_all_data()

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

python github_pipeline.py

4. Inspecting your load result​

You can now inspect the state of your pipeline with the dlt cli:

dlt pipeline github_events 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 github_events 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 pipelines using Github Actions. This is a CI/CD runner that you can use basically for free.
  • Deploy with Airflow: You can deploy your dlt pipelines using Airflow. This method will create an Airflow DAG for your pipeline script that you should customize.
  • Deploy with Google Cloud Functions: dlt also enables you to deploy your pipelines using Google Cloud Functions. This allows you to run your pipeline in a serverless environment.
  • Other Deployment Methods: dlt supports various other deployment methods. You can find more information on these methods here.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides tools to help you monitor your pipeline's performance and status. This includes viewing load information and saving it to your destination, inspecting schema changes, and more. Learn how to monitor your pipeline.
  • Set up alerts: Stay informed about your pipeline's status by setting up alerts. dlt allows you to set up alerts for failed jobs, schema changes, and more. Find out how to set up alerts.
  • Set up tracing: Tracing is another powerful feature that dlt provides to help you keep track of your pipeline's performance. It provides detailed timing information on extract, normalize, and load steps. Learn how to set up tracing.

Available Sources and Resources​

For this verified source the following sources and resources are available

Source github_repo_events​

"GitHub repo events source provides data about activities and interactions within a repository."

Resource NameWrite DispositionDescription
repo_eventsappendRetrieves all the repository events associated with the GitHub repository. This includes information about the actor (user who triggered the event), organization, payload (specific details about the event), and the repository itself.

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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