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Loading GitHub Data to Dremio using Python's dlt Library

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Welcome to this technical guide on how to utilize dlt, an open-source Python library, to load data from GitHub to Dremio. This guide will show you how to use a verified source to fetch data on issues, pull requests, or events from any GitHub repository and load it onto a Dremio destination of your choice using the GitHub API. Dremio is a comprehensive data lakehouse solution, offering flexibility, scalability, and performance to meet leaders at all stages of their data journey. For more information on using the GitHub API, please refer to the official documentation at https://docs.github.com. Let's dive into the dlt world!

dlt Key Features​

  • Robust Governance Support: dlt pipelines provide robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more about these features here.

  • 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. Find out more here.

  • Efficient Data Extraction: dlt simplifies the data extraction process by using loading or incremental extraction metadata. It also ensures scalability through iterators, chunking, and parallelization. Learn more here.

  • Community Support: dlt has a growing community where you can find recent releases or discuss what you can build with dlt. You can join the community here.

  • Ease of Deployment: dlt provides commands to make deployment easier, whether from a branch of the verified-sources repo or another repo. Find out more about this 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 Dremio:

pip install "dlt[dremio]"

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

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github dremio
# 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[dremio]>=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.dremio]
dataset_name = "dataset_name" # please set me up!
staging_data_source = "staging_data_source" # please set me up!

[destination.dremio.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!
port = 32010

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 Dremio 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='dremio',
        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='dremio', 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='dremio',
        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 Dremio 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 supports deployment through Github Actions. It is a CI/CD runner that can be used for free. You can specify when the GitHub Action should run using a cron schedule expression.
  • Deploy with Airflow: Another way to deploy your dlt pipeline is by using Airflow. Particularly, Google Composer, a managed Airflow environment provided by Google, can be used for this purpose.
  • Deploy with Google Cloud Functions: dlt can also be deployed using Google Cloud Functions. This allows you to run your pipelines on Google's serverless platform.
  • Explore Other Deployment Options: There are other ways to deploy your dlt pipeline. You can explore these options here.

The running in production section will teach you about:​

  • Monitoring your pipeline: dlt provides detailed monitoring capabilities to keep track of your pipeline's performance and health. Check out the guide on how to monitor your pipeline for more details.
  • Setting up alerts: Stay informed about any issues or anomalies in your pipeline with dlt's alerting feature. Learn how to set up alerts with dlt.
  • Implementing tracing: Trace the execution of your pipeline and identify bottlenecks or areas for optimization. The guide on how to set up tracing provides a detailed walkthrough.

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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