Today, Jupyter Notebooks are mostly confined to science, research & education. But notebooks can provide organizations with a powerful general-purpose “executable documentation” platform. A solid use case for this is DevOps & more specifically, IT incident response.
Technology teams usually have an on-call rotation with static wiki-style documentation to guide the on-call engineer. Jupyter Notebooks can replace static documentation with executable notebooks. E.g. “fetch service logs” and “rollback last deployment” can simply mean executing a code cell that’s available alongside the markdown instructions.
What are the benefits of executable vs. static documentation for DevOps -
Quick e.g. “check DB latency” is 1-click notebook code cell execution to plot latency graph vs. going to a third-party UI in the middle of an incidence
Precise e.g. “promote read replica to master” can mean a series of steps & possibility of human error; codifying the steps in advance removes ambiguity & results in precise action.
“Executable documentation” is a simple yet powerful concept that can extend to other use cases such as - API documentation, developer onboarding, data visualization & reporting, scheduling routine tasks & so on. Think of it as executable GoogleDocs powered by Jupyter!
In this talk, we’d like to,
- Introduce the concept of Jupyter powered “executable documentation” platform, particularly for DevOps and Incident Response,
- Show a demo of how it’d work - ( • API Latency Demo )
- Talk about important challenges, and propose a way forward to make this a mainstream application of Jupyter notebooks.
21 авг 2024
