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Python Guide: Load GitHub Data to Azure Synapse using dlt Library

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Welcome to the technical documentation on data loading from github to azure synapse using the open-source Python library dlt. This guide will walk you through how to leverage dlt to load data on issues, pull requests, or events from any github repository to azure synapse, an analytics service that combines enterprise data warehousing and Big Data analytics. You will learn how to use the github API for data extraction and dlt for data transformation and loading to azure synapse. For more information on the github API, please refer to GitHub Documentation.

dlt Key Features​

  • Azure Synapse Integration: dlt can be easily integrated with Azure Synapse, a powerful data warehouse. Install the dlt library with Synapse dependencies using pip install dlt[synapse]. Learn more
  • Tutorial: dlt provides a comprehensive tutorial to guide you on how to build a data pipeline efficiently. The tutorial covers everything from fetching data from the GitHub API to making reusable data sources. Follow the tutorial
  • Advanced Usage: dlt supports advanced usage scenarios like deploying from a branch of a repo or from another repo. Use dlt init --help to find out more about this command. Learn more
  • Governance Support: dlt pipelines offer robust governance support through mechanisms like pipeline metadata utilization, schema enforcement and curation, and schema change alerts. Read more
  • Getting Started Guide: After understanding the basics of dlt, you can dive deeper by following the detailed tutorial provided in the Getting Started Guide. The guide also provides resources on topics like creating a pipeline, configuring DuckDB, and exploring the data. Get started

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 Azure Synapse:

pip install "dlt[synapse]"

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

# create a new directory
mkdir github_pipeline
cd github_pipeline
# initialize a new pipeline with your source and destination
dlt init github synapse
# 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[synapse]>=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.synapse]
create_indexes = false
default_table_index_type = "heap"
staging_use_msi = false

[destination.synapse.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 = 1433
connect_timeout = 15
driver = "driver" # 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 Azure Synapse 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='synapse',
        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='synapse', 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='synapse',
        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 Azure Synapse 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: Github Actions is a CI/CD runner that is virtually free to use. You can deploy your pipeline using the dlt deploy command and specify when the Github Action should run using a cron schedule expression. Learn more about it here.
  • Deploy with Airflow: Google Composer is a managed Airflow environment provided by Google. You can deploy your pipeline using the dlt deploy command and it will create an Airflow DAG for your pipeline script. Learn more about it here.
  • Deploy with Google Cloud Functions: Google Cloud Functions is a serverless execution environment for building and connecting cloud services. You can deploy your pipeline using the dlt deploy command and it will create a Google Cloud Function for your pipeline script. Learn more about it here.
  • Other Deployment Options: There are various other ways to deploy your pipeline, such as with AWS Lambda, Azure Functions, and more. You can explore these other options here.

The running in production section will teach you about:​

  • Monitor Your Pipeline: The monitoring guide provides detailed instructions on how to keep an eye on the performance and status of your pipeline, ensuring that everything runs smoothly and efficiently.
  • Set Up Alerts: With the alerting guide, you can configure alerts to notify you of any significant events or issues that occur during the execution of your pipeline. This helps you to respond promptly to any problems and maintain the health of your pipeline.
  • Implement Tracing: The tracing guide offers insights on how to set up tracing in your pipeline. Tracing allows you to track the flow of your pipeline and identify any bottlenecks or issues that might be affecting its performance.

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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