Skip to main content

Loading GitHub Data to MotherDuck using Python and dlt Library

Need help deploying these pipelines, or figuring out how to run them in your data stack?

Join our Slack community or book a call with our support engineer Adrian.

Welcome to this technical documentation. Here, we will discuss how to use the dlt Python library to load data from GitHub onto MotherDuck. GitHub serves as our verified source, allowing us to extract data such as issues, pull requests, or events from any GitHub repository. Our chosen destination, MotherDuck, is a fast in-process analytical database with a feature-rich SQL dialect and deep integrations into client APIs. Using dlt, we can efficiently transfer data between these platforms. For further details on the GitHub source, please visit this link.

dlt Key Features​

  • Efficient Data Pipeline Creation: dlt allows you to build efficient data pipelines with ease. It provides a step-by-step tutorial to guide you through the process of loading data from the GitHub API into DuckDB. Tutorial
  • MotherDuck Destination: dlt provides a destination called MotherDuck, which is still in the testing phase but offers a robust data loading solution. It supports all write dispositions and integrates with dbt. MotherDuck Destination
  • Advanced Initialization: dlt offers advanced initialization options, allowing you to deploy pipelines from different branches, local folders or git repositories. Advanced Initialization
  • Comprehensive Resources: dlt provides a wealth of resources to help you understand and use the tool effectively. From creating and running a pipeline to configuring DuckDB and exploring data, dlt has got you covered. Resources
  • Verified GitHub Source: dlt offers a verified source for the GitHub API, allowing you to load data on issues or pull requests from any GitHub repository onto a destination of your choice. GitHub Source

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 MotherDuck:

pip install "dlt[motherduck]"

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

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

You now have the following folder structure in your project:

my-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:

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

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.motherduck.credentials]
database = "database" # please set me up!
password = "password" # please set me up!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the MotherDuck destination in the dlt destinations documentation.

Likewise you can find the setup instructions for GitHub source in the dlt verifed sources documentation.

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='motherduck',
        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='motherduck', 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='motherduck',
        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 MotherDuck 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 free CI/CD runner that you can use for your project. Learn more about it here.
  • Deploy with Airflow: You can also deploy your dlt pipelines with Airflow. This option is great if you are using Google's managed Airflow environment, Google Composer. Find out how to do it here.
  • Deploy with Google Cloud Functions: If you are using Google Cloud, you can deploy your dlt pipelines with Google Cloud Functions. This serverless execution environment allows you to build and connect cloud services with code. Learn more about this deployment option here.
  • Other Deployment Options: dlt offers a range of other deployment options for your pipelines. You can explore them all here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: With dlt, you can easily monitor your pipeline. This feature allows you to track the progress of your data pipeline and identify any potential issues that may arise. For more information, check out the guide on How to Monitor your pipeline.
  • Set Up Alerts: dlt also allows you to set up alerts. This will enable you to receive notifications about the status of your pipeline, ensuring you are always informed about its performance. Learn more about how to Set up alerts.
  • Set Up Tracing: Tracing is another essential feature in dlt. It provides you with detailed information about the execution of your pipeline, helping you to understand how your data is processed. To get started with tracing, read the guide on Setting 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!

DHelp

Ask a question

Welcome to "Codex Central", your next-gen help center, driven by OpenAI's GPT-4 model. It's more than just a forum or a FAQ hub – it's a dynamic knowledge base where coders can find AI-assisted solutions to their pressing problems. With GPT-4's powerful comprehension and predictive abilities, Codex Central provides instantaneous issue resolution, insightful debugging, and personalized guidance. Get your code running smoothly with the unparalleled support at Codex Central - coding help reimagined with AI prowess.