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Python Data Loading from github to aws s3 using dlt Library

Connecting other file destinations

This document describes how to set up loading to aws 3, but our filesystem source can not only load to s3, but also to Google Cloud Storage, Google Drive, Azure, or local filesystem. Learn more about this here.

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Welcome to our technical guide on utilizing the open-source Python library dlt for data loading operations. This guide will specifically focus on using dlt to load data from GitHub onto AWS S3. The GitHub verified source allows you to load data on issues, pull requests, or events from any GitHub repository using the GitHub API. As for AWS S3, it serves as a filesystem destination, storing data in remote file systems and bucket storages. It uses fsspec to abstract file operations, and while it's primarily used as a staging area for other destinations, it can also be used to quickly build a data lake. For more information on the GitHub source, visit https://docs.github.com.

dlt Key Features​

  • Advanced Usage with dlt init: The dlt init command allows you to deploy from a branch of a repo, or from another repo by providing the new repo url. Learn more here.
  • Comprehensive Tutorial: The tutorial provides a step-by-step guide on how to use dlt to build a data pipeline, covering topics from fetching data from an API to making reusable data sources. Get started here.
  • Verified Source for GitHub API: dlt provides a verified source for loading data from the GitHub API onto a destination of your choice. Discover more here.
  • Secure Handling of Secrets: dlt supports different formats for keys and provides secure handling of sensitive information through environment variables and TOML files. Dive deeper here.
  • Support for Filesystem & Buckets: dlt can store data in remote file systems and bucket storages like S3, Google Storage, or Azure Blob Storage. Learn how to install dlt with filesystem dependencies 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 AWS S3:

pip install "dlt[filesystem]"

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 S3. You can run the following commands to create a starting point for loading data from GitHub to AWS S3:

# create a new directory
mkdir my-github-pipeline
cd my-github-pipeline
# initialize a new pipeline with your source and destination
dlt init github filesystem
# 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[filesystem]>=0.3.25

You now have the following folder structure in your project:

my-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:

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

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.filesystem]
bucket_url = "bucket_url" # please set me up!

[destination.filesystem.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!
Further help setting up your source and destinations

Please consult the detailed setup instructions for the AWS S3 destination in the dlt destinations documentation.

Likewise you can find the setup instructions for GitHub source in the dlt verifed sources documentation.

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='filesystem',
        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='filesystem', 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='filesystem',
        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 S3 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 allows you to easily prepare your pipeline for deployment using Github Actions. Github Actions is a CI/CD runner that runs your pipeline at specified times. Learn how to deploy a pipeline with Github Actions here.
  • Deploy with Airflow: You can deploy your dlt pipeline using Google's managed Airflow environment, Google Composer. The dlt deploy command creates an Airflow DAG for your pipeline script, making the deployment process trivial. Find out more about deploying a pipeline with Airflow here.
  • Deploy with Google Cloud Functions: dlt also supports deployment with Google Cloud Functions. This allows you to execute your code in response to events without having to manage a server. Learn how to deploy a pipeline with Google Cloud Functions here.
  • Other Deployment Options: dlt supports various other deployment options. You can find more information about these deployment options here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: dlt provides features to help you monitor your data pipeline in production. You can inspect and save load info, trace runtime, and alert on schema changes. Learn more about how to Monitor your pipeline.
  • Set Up Alerts: Alerts are a crucial part of running a data pipeline in production. dlt enables you to set up alerts for schema changes and other critical events. Check out how to Set up alerts.
  • Set Up Tracing: Tracing helps you understand the flow and performance of your data pipeline. With dlt, you can set up tracing to get detailed information on extract, normalize, and load steps. Get started with Setting 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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