Top ETL Tools for GitHub Integration to follow in 2024

Here is our recommendation for the criteria to consider:

• Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
• Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
• Ability to build new connectors: all data integration solutions support a limited number of data sources.
• Support of change data capture: this is especially important for your databases.
• Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
• Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
• Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ? 
• Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an square integration with dbt
• Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
• Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
• Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.

1. Airbyte

Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.

What's unique about Airbyte?

Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required.. 

Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data. 

Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.

GitHub's API provides access to a wide range of data related to repositories, users, organizations, and more. Some of the categories of data that can be accessed through the API include:  

- Repositories: Information about repositories, including their name, description, owner, collaborators, issues, pull requests, and more.

- Users: Information about users, including their username, email address, name, location, followers, following, organizations, and more.

- Organizations: Information about organizations, including their name, description, members, repositories, teams, and more.

- Commits: Information about commits, including their SHA, author, committer, message, date, and more.

- Issues: Information about issues, including their title, description, labels, assignees, comments, and more.

- Pull requests: Information about pull requests, including their title, description, status, reviewers, comments, and more.

- Events: Information about events, including their type, actor, repository, date, and more.  

Overall, the GitHub API provides a wealth of data that can be used to build powerful applications and tools for developers, businesses, and individuals.

1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.

2. Click on the "GitHub" source connector and select "Create a new connection."

3. Enter a name for the connection and click "Next."

4. Enter your GitHub credentials, including your username and personal access token. If you do not have a personal access token, you can create one by following the instructions provided in the Airbyte documentation.

5. Select the repositories you want to connect to Airbyte and click "Test Connection" to ensure that the connection is successful.

6. Once the connection is successful, click "Create Connection" to save the connection.

7. You can now use the GitHub source connector to extract data from your selected repositories and integrate it with other data sources in Airbyte.

Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.

And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.