Skip to main content

Python Data Loading from GitHub to Redshift Using 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 Violetta.

This document provides instructions on how to use the open-source Python library, dlt, to load data from GitHub onto Amazon Redshift. GitHub is a verified source that allows the extraction of data on issues, pull requests, or events from any GitHub repository using the GitHub API. Amazon Redshift is a fully managed, petabyte-scale data warehouse service that can handle data scaling from a few hundred gigabytes to a petabyte or more. This guide will help you leverage dlt to efficiently transfer data from GitHub to Redshift. For more information on the GitHub API, visit https://docs.github.com.

dlt Key Features​

  • GitHub API Integration: The GitHub verified source can be used to load data on issues or pull requests from any GitHub repository onto a destination of your choice using the GitHub API.

  • Tutorial: A comprehensive tutorial is provided to guide users on how to efficiently use dlt to build a data pipeline. The tutorial covers various topics like fetching data from the GitHub API, understanding and managing data loading behaviors, incrementally loading new data, and securely handling secrets.

  • Advanced Usage: dlt supports advanced usage scenarios such as deploying from a branch of the verified-sources repo or deploying from another repo. More details can be found in the Add a Verified Source section.

  • Governance Support: dlt pipelines offer robust governance support through three key mechanisms: pipeline metadata utilization, schema enforcement and curation, and schema change alerts. More details can be found in the Build a Pipeline Tutorial section.

  • Amazon Redshift Integration: The dlt library supports Amazon Redshift as a destination. The Amazon Redshift Destination guide provides detailed instructions on how to set up and use Redshift as a destination in your data pipeline.

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 Redshift:

pip install "dlt[redshift]"

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

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github redshift
# 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[redshift]>=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.redshift.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 = 5439
connect_timeout = 15

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 Redshift 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='redshift',
        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='redshift', 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='redshift',
        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 Redshift 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 simple way to deploy your pipeline using Github Actions. You can schedule the action using a cron schedule expression. Read more
  • Deploy with Airflow: With dlt, you can easily deploy your pipeline using Airflow, a platform used to programmatically author, schedule and monitor workflows. Read more
  • Deploy with Google Cloud Functions: dlt allows you to deploy your pipeline using Google Cloud Functions, a serverless execution environment for building and connecting cloud services. Read more
  • Other Deployment Options: dlt supports various deployment options to suit your needs, including AWS Lambda, Google Composer, and more. Read more

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides comprehensive tools to inspect and monitor your data pipeline. You can track the progress of your data loads, inspect the pipeline, and save load info and traces. Learn more about it here.
  • Set Up Alerts: With dlt, you can set up alerts to notify you of any schema changes in your data. This ensures that you are always aware of any changes that might affect your data pipeline. Find out how to set this up here.
  • Set Up Tracing: dlt allows you to trace the runtime of your pipeline. This includes timing information on extract, normalize, and load steps, as well as all the config and secret values. 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!

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.