Building a Python SDK for Azure Databricks

Building a Python SDK for Azure Databricks

Building a Python SDK for Azure Databricks. This article is about a new project I started to work on lately. Please welcome Azure Databricks SDK Python.

This article will present the project, the current progress, release plan, some design choices, and at final dev process/tools.

I spent several nights working and searching for best practices to implement this SDK. I am convinced that It will need more than my humble efforts to become stable and usable in production. Therefore, my friends, all contributions are welcome! Do not hesitate to reach to me if you want to contribute by any means (docs, code, testing, etc).


I will start by my own use-cases:

  • Mix and match: When working on a competitive field, you will have to cope with having new tools and platforms popping out from nowhere. I like to have simple means to mix my most-used tools with the brand-new ones. Using APIs to integrate directly is time-consuming, which leads in most cases to hacks and error-prone automation.
  • Keeping evolving: Usually, the ecosystem does not integrate the preview features. For example, Data Factory still does not include the docker options (DCS) when creating a cluster in the Databricks activity. Sometimes, you need to force the update. An SDK could be very useful to build custom connectors.
  • Custom bricks: You want to build on top of Azure Databricks: let’s imagine You have a great idea that you must prototype as fast as you can, for a startup or a hackathon. If some of its blocks could be done by Azure Databricks, an SDK will help you do it in no time. You will be able to bundle everything in your app or package (et voilà !).
I think It’s plenty to justify the need. Nonetheless, I am quite sure there should be additional use-cases. I’ll be excited to hear yours in the comments (on medium).

databricks azure microsoft package-design python

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