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Load GitHub Data to EDB BigAnimal Using dlt in Python

tip

We will be using the dlt PostgreSQL destination to connect to EDB BigAnimal. You can get the connection string for your EDB BigAnimal database as described in the EDB BigAnimal Docs.

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This documentation provides guidance on loading data from GitHub repositories to EDB BigAnimal using the open-source Python library dlt. You can use this verified source to extract data on issues, pull requests, or events from any GitHub repository via the GitHub API. EDB BigAnimal is a fully managed database service that operates in your cloud account or in BigAnimal's cloud account, managed by the creators of Postgres. It simplifies the setup, management, and scaling of databases, offering options like PostgreSQL, EDB Postgres Advanced Server with Oracle compatibility, and distributed high-availability clusters. For more detailed information on the GitHub API, visit GitHub documentation.

dlt Key Features​

  • Fetching data from the GitHub API: Learn how to retrieve data from the GitHub API and load it into your data pipeline. Read more
  • Managing data loading behaviors: Understand how to control and manage the data loading process, including appending or replacing data. Read more
  • Incremental loading and deduplication: Learn how to incrementally load new data and deduplicate existing data to ensure data consistency. Read more
  • Dynamic data fetching and code reduction: Discover techniques to make your data fetch more dynamic and reduce redundancy in your code. Read more
  • Handling secrets securely: Learn how to manage and handle sensitive information securely within your data pipeline. 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 EDB BigAnimal:

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

# 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:

dlt[postgres]>=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.postgres]
dataset_name = "dataset_name" # please set me up!

[destination.postgres.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 = 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 EDB BigAnimal 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, github_stargazers


def load_duckdb_repo_reactions_issues_only() -> None:
    """Loads issues, their comments and reactions for duckdb"""
    pipeline = dlt.pipeline(
        "github_reactions",
        destination='postgres',
        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='postgres', 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='postgres',
        dataset_name="dlthub_reactions",
        full_refresh=True,
    )
    data = github_reactions("dlt-hub", "dlt")
    print(pipeline.run(data))


def load_dlthub_dlt_stargazers() -> None:
    """Loads all stargazers for dlthub dlt repo"""
    pipeline = dlt.pipeline(
        "github_staragarzers",
        destination='postgres',
        dataset_name="dlthub_staragarzers",
        full_refresh=True,
    )
    data = github_stargazers("dlt-hub", "dlt")
    print(pipeline.run(data))


if __name__ == "__main__":
    load_duckdb_repo_reactions_issues_only()
    load_airflow_events()
    load_dlthub_dlt_all_data()
    load_dlthub_dlt_stargazers()

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 EDB BigAnimal 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 for CI/CD. Follow the guide here.

  • Deploy with Airflow and Google Composer: This guide helps you deploy a dlt pipeline with Airflow, specifically using Google Composer. Check it out here.

  • Deploy with Google Cloud Functions: Explore how to deploy your dlt pipeline using Google Cloud Functions for a serverless approach. Follow the instructions here.

  • More Deployment Options: Discover additional methods to deploy your dlt pipeline, including other cloud services and platforms. Learn more here.

The running in production section will teach you about:​

  • How to Monitor your pipeline: Learn how to monitor the performance and status of your dlt pipeline in production. How to Monitor your pipeline
  • Set up alerts: Configure alerting mechanisms to notify you of any issues or important events in your dlt pipeline. Set up alerts
  • Set up tracing: Implement tracing to get detailed insights into the execution flow and performance of your dlt pipeline. And 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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