How much time do you spend solving negative engineering problems? And can a framework solve them for you?

Think of negative engineering as things you do to avoid bad outcomes in software. At the lowest level,

this can be writing good error handling with try except. But it's broader than that, logging,

observability, like with Century Tools, retries, failovers, as in what you might get from Kubernetes, and so on.

We have a great chat with Chris White about Prefect, a tool for data engineers and data scientists, meaning to solve these problems automatically.

It's also a conversation, I think, is applicable to the broader software development community as well.

This is Talk Python, and Me. Episode 365, recorded May 9th, 2022.

Welcome to Talk Python, a weekly podcast on Python. This is your host, Michael Kennedy. Follow me on Twitter where I'm at M. Kennedy and keep up with a show and listen to past episodes at Talk Python.fm. And follow the show on Twitter via at Talk Python. We've started streaming most of our episodes live on YouTube.

Subscribe to our YouTube channel over at TalkPython.fm slash YouTube to get notified about upcoming shows and be part of that episode.

This episode is sponsored by Microsoft for Startups Founders Hub.

Check them out at TalkPython.fm slash Founders Hub to get early support

for your startup. And it's brought to you by us over at Talk Python Training. Did you know we have

one of the largest course libraries for Python courses? They're all available without a subscription.

So check it out over at Talk Python.fm. Just click on courses.

Transcripts for this and all of our episodes are brought to you by AssemblyAI. Do you need a great

automatic speech-to-text API? Get human-level accuracy in just a few lines of code. Visit

talk python.fm slash assembly AI. Chris, welcome to Talk Python to me.

Yeah, thanks for having me.

Happy to be here.

I'm happy to have you here.

It's going to be a lot of fun.

We're going to talk a lot of data engineering things.

...