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Loading GitHub Data to AlloyDB Using dlt in Python


We will be using the dlt PostgreSQL destination to connect to AlloyDB. You can get the connection string for AlloyDB from the GCP AlloyDB Console.

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This documentation will guide you on how to load data from GitHub into AlloyDB using the dlt library. The dlt library allows you to extract data on issues, pull requests, or events from any GitHub repository and load it into AlloyDB, a fully managed, PostgreSQL-compatible database service. AlloyDB is designed to handle demanding workloads, including hybrid transactional and analytical processing, offering enterprise-grade performance, reliability, and availability. By leveraging the dlt library, you can efficiently manage and analyze your GitHub data within AlloyDB. For more details on the GitHub API, visit GitHub Documentation.

dlt Key Features

  • Automated Maintenance: With schema inference and evolution, alerts, and short declarative code, maintenance becomes simple. Learn more
  • Scalability: dlt offers scalable data extraction by leveraging iterators, chunking, and parallelization techniques. Read about scalability
  • User-friendly Interface: A declarative interface that removes knowledge obstacles for beginners while empowering senior professionals. Get started
  • Secure Authentication: Supports multiple authentication types like password, key pair, and external authentication for Snowflake. Explore authentication
  • Community Support: Join the dlt community on Slack, report issues on GitHub, and get help from support engineers. 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 AlloyDB:

pip install "dlt[postgres]"

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

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github postgres
# 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:


You now have the following folder structure in your project:

├── .dlt/
│ ├── config.toml # configs for your pipeline
│ └── secrets.toml # secrets for your pipeline
├── github/ # folder with source specific files
│ └── ...
├── # 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

log_level="WARNING" # the system log level of dlt
# use the dlthub_telemetry setting to enable/disable anonymous usage data reporting, see
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

access_token = "access_token" # please set me up!

dataset_name = "dataset_name" # please set me up!

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 = 5432
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 AlloyDB 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, 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(
# get only 100 items (for issues and pull request)
data = github_reactions(
"duckdb", "duckdb", items_per_page=100, max_items=100

def load_airflow_events() -> None:
"""Loads airflow events. Shows incremental loading. Forces anonymous access token"""
pipeline = dlt.pipeline(
"github_events", destination='postgres', dataset_name="airflow_events"
data = github_repo_events("apache", "airflow", access_token="")
# if you uncomment this, it does not load the same events again
# data = github_repo_events("apache", "airflow", access_token="")
# print(

def load_dlthub_dlt_all_data() -> None:
"""Loads all issues, pull requests and comments for dlthub dlt repo"""
pipeline = dlt.pipeline(
data = github_reactions("dlt-hub", "dlt")

if __name__ == "__main__":

Provided you have set up your credentials, you can run your pipeline like a regular python script with the following command:


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 AlloyDB 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: Learn how to deploy your dlt pipeline using Github Actions.
  • Deploy with Airflow and Google Composer: Follow this guide to deploy your dlt pipeline using Airflow and Google Composer.
  • Deploy with Google Cloud Functions: This walkthrough will help you deploy your dlt pipeline using Google Cloud Functions.
  • Explore other deployment options: Discover more ways to deploy your dlt pipeline by checking out the additional deployment methods.

The running in production section will teach you about:

  • How to Monitor your pipeline: Learn how to effectively monitor your dlt pipelines to ensure smooth and error-free operations. How to Monitor your pipeline
  • Set up alerts: Set up alerts to stay informed about the status and performance of your dlt pipelines. Set up alerts
  • Set up tracing: Implement tracing to get detailed insights into the execution of your dlt pipelines. 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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