The notebook format that has been exemplified by the IPython/Jupyter project has gained in popularity among data scientists. While the existing formats have proven their value, they are still susceptible with difficulties in collaboration and maintainability. Scott Ernst created the Cauldron notebook to be testable, production ready, and friendly to version control. This week we explore the capabilities, use cases, and architecture of Cauldron and how you can start using it today!
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- Your host as usual is Tobias Macey and today I’m interviewing Scott Ernst about Cauldron, a new notebook format built with software engineering best practices in mind.
- How did you get introduced to Python?
- Can you start by explaining what Cauldron is and what problem you were trying to solve when you created it?
- In the documentation it mentions that you can use any editor for creating the content of the notebook. Can you describe a typical workflow of authoring the various files and cells and viewing the output?
- How does Cauldron compare to the Jupyter notebook format and what factors would lead someone to choose one over the other?
- Does Cauldron support running languages other than Python? If not then what would be involved in adding that capability?
- Cauldron notebooks support unit tests of individual cells. How does that process work and what are the limitations?
- The option for running the notebook in the context of a task workflow tool appears to be a powerful capability. What are some of the considerations that are necessary when writing a notebook to be run in that manner?
- What are some of the most interesting or unexpected projects that you have seen people using Cauldron for?
- What do you have planned for the future of Cauldron?
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