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Using Python and dlt to Load GitHub Data into AWS Athena

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Welcome to this technical documentation about utilizing dlt, an open-source Python library, to load data from GitHub onto AWS Athena. This guide will demonstrate how to fetch data on issues, pull requests, or events from any GitHub repository using the GitHub API. AWS Athena is an interactive query service that allows easy analysis of data in Amazon S3 using standard SQL, with our implementation also supporting iceberg tables. For more information on the GitHub source, visit GitHub's Documentation.

dlt Key Features​

  • Tutorial: A comprehensive guide that introduces you to the foundational concepts of dlt and walks you through basic and advanced usage scenarios. The tutorial uses a practical example of building a data pipeline that loads data from the GitHub API into DuckDB. Learn more
  • Advanced Usage: dlt allows for advanced usage scenarios such as deploying from a branch of a verified-sources repo or deploying from a forked repo. This offers flexibility and control in managing your data pipelines. Learn more
  • Governance Support: dlt pipelines offer robust governance support through pipeline metadata utilization, schema enforcement and curation, and schema change alerts. These features contribute to better data management practices and overall data governance. Learn more
  • Athena Destination: The dlt library provides an Athena destination which stores data as parquet files in S3 buckets and creates external tables in AWS Athena. This destination is especially useful for AWS users who wish to leverage the power of Athena for their data analysis tasks. Learn more
  • Getting Started Guide: A resourceful guide that provides a step-by-step walkthrough on how to get started with dlt. The guide covers topics like creating a pipeline, running a pipeline, configuring DuckDB, and exploring the data. Learn 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 AWS Athena:

pip install "dlt[athena]"

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

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

[destination.athena.credentials]
aws_access_key_id = "aws_access_key_id" # please set me up!
aws_secret_access_key = "aws_secret_access_key" # 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 AWS Athena 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='athena',
        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='athena', 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='athena',
        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 AWS Athena 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 enables seamless deployment with Github Actions. By running the command dlt deploy <script>.py github-action --schedule "*/30 * * * *", you can set up a CI/CD pipeline that runs on a schedule. Learn more about it here.
  • Deploy with Airflow: dlt supports deployment with Airflow. With the command dlt deploy <script>.py airflow-composer, you can create an Airflow DAG for your pipeline script. Find out more about deploying with Airflow here.
  • Deploy with Google Cloud Functions: You can also deploy your dlt pipeline with Google Cloud Functions. Check out the guide on how to deploy a pipeline with Google Cloud Functions here.
  • Other Deployment Options: dlt provides flexibility with various deployment options. Explore more about other ways to deploy a pipeline here.

The running in production section will teach you about:​

  • Monitor your pipeline: dlt provides you with the ability to monitor your pipeline in real-time, helping you keep track of data loads, errors, and performance. Find out more on how to monitor your pipeline.
  • Set up alerts: With dlt, you can set up alerts to be notified about any issues or changes in your pipeline. This feature allows you to respond quickly to any potential problems. Learn more about how to set up alerts.
  • Set up tracing: Tracing in dlt helps you understand the execution flow of your pipeline. It provides detailed information about each step, allowing you to identify and fix any issues quickly. Discover 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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