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Python Guide: Use dlt to Load GitHub Data into BigQuery

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This page provides technical documentation on how to use the open-source Python library dlt to load data from GitHub to BigQuery. With dlt, you can extract data on issues, pull requests, or events from any GitHub repository using the GitHub API and load it onto BigQuery, a serverless, cost-effective enterprise data warehouse. This enables comprehensive data analysis across clouds, scaling with your data needs. For more information on the GitHub source, refer to the GitHub Documentation.

dlt Key Features​

  • Automated Maintenance: dlt offers automated maintenance features such as schema inference and evolution and alerts. This makes maintenance simple and straightforward. Learn more here.
  • Versatility: dlt can run wherever Python runs - on Airflow, serverless functions, notebooks, and more. It doesn't require any external APIs, backends, or containers and can scale on both micro and large infrastructure. Find out more here.
  • User-friendly Interface: dlt provides a user-friendly, declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Dive into the Getting Started Guide to explore dlt's interface.
  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: 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. Read more about it here.
  • Tutorial and Community Support: dlt offers a detailed tutorial to guide users on how to efficiently use dlt to build a data pipeline. Additionally, dlt has a supportive community where users can ask questions, share their use cases, and learn from each other. Check out the Tutorial and join the Community.

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 BigQuery:

pip install "dlt[bigquery]"

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

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github bigquery
# 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[bigquery]>=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.bigquery]
location = "US"

[destination.bigquery.credentials]
project_id = "project_id" # please set me up!
private_key = "private_key" # please set me up!
client_email = "client_email" # 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 BigQuery 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='bigquery',
        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='bigquery', 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='bigquery',
        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 BigQuery 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 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.
  • Deploy with Airflow: You can also deploy dlt with Airflow. This is a managed environment provided by Google. It will create an Airflow DAG for your pipeline script that you should customize.
  • Deploy with Google Cloud Functions: dlt can be deployed using Google Cloud Functions. This allows you to run your code without having to manage a server.
  • Other Deployment Options: There are many other ways to deploy dlt. You can explore more options here.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides built-in tools for monitoring your pipeline. You can view the status of your pipeline, inspect the data, and check the logs for any errors. Learn more about monitoring your pipeline here.
  • Set up alerts: With dlt, you can set up alerts to notify you of any issues with your pipeline. This can help you identify and address problems quickly, ensuring your pipeline runs smoothly. Find out how to set up alerts here.
  • Set up tracing: Tracing allows you to track the flow of data through your pipeline. This can help you identify bottlenecks and optimize your pipeline for better performance. Learn how to set up tracing here.

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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