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Python Data Loading from GitHub to Databricks using dlt Library

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Welcome to the technical guide on loading data from GitHub to Databricks using the open-source Python library, dlt. This guide will help you extract data on issues, pull requests, or events from any GitHub repository and load it into your preferred destination in Databricks, a unified data analytics platform. dlt simplifies this process, making it easy for data science, engineering, and business professionals to accelerate innovation. For more information on the GitHub source, please refer to the official GitHub Documentation.

dlt Key Features​

  • Automated Maintenance: dlt provides automated maintenance with schema inference and evolution and alerts. With short declarative code, maintenance becomes simple. Read More
  • Run it Where Python Runs: dlt can run on Airflow, serverless functions, notebooks, and more. It doesn't require external APIs, backends, or containers, and scales on micro and large infrastructures. Read More
  • Declarative Interface: dlt offers a user-friendly, declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Read More
  • Robust Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read More
  • Scaling and Finetuning: dlt offers several mechanisms and configuration options to scale up and finetune pipelines. This includes running extraction, normalization, and load in parallel, writing sources and resources that run in parallel via thread pools and async execution, and finetuning memory buffers, intermediary file sizes, and compression options. Read 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 Databricks:

pip install "dlt[databricks]"

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

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github databricks
# 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[databricks]>=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.databricks.credentials]
catalog = "catalog" # please set me up!
server_hostname = "server_hostname" # please set me up!
http_path = "http_path" # please set me up!
access_token = "access_token" # 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 Databricks 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='databricks',
        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='databricks', 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='databricks',
        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 Databricks 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 provides a straightforward way to deploy your pipeline using Github Actions. This involves setting a cron schedule expression and specifying additional flags.
  • Deploy with Airflow: You can also deploy your pipeline using Airflow, a task scheduler platform. dlt simplifies this process by creating an Airflow DAG for your pipeline script.
  • Deploy with Google Cloud Functions: dlt allows for deployment with Google Cloud Functions, a serverless execution environment for building and connecting cloud services.
  • And More: There are more ways to deploy your pipeline using dlt. Visit this page to explore other deployment options.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides you with the necessary tools to monitor your pipeline effectively. It allows you to inspect and save the load info and trace, inspect, save and alert on schema changes. For more details, check out the guide on how to monitor your pipeline.
  • Set up alerts: With dlt, you can set up alerts to notify you of any issues or changes in your pipeline. This feature helps you to maintain the health of your pipeline and ensures that you are always up-to-date with its status. Learn more about how to set up alerts.
  • Set up tracing: Tracing in dlt enables you to track the execution of your pipeline and helps in debugging and optimizing your pipeline. It provides you with detailed information about the execution of your pipeline. Find out more on 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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