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 Sentry tools), retries, failover (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 many of these problems automatically. But it's a conversation applicable to a broader software development community as well.<br/>
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<strong>Links from the show</strong><br/>
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<div><b>Chris White</b>: <a href="https://twitter.com/markov_gainz" target="_blank" rel="noopener">@markov_gainz</a><br/>
<b>Prefect</b>: <a href="https://www.prefect.io" target="_blank" rel="noopener">prefect.io</a><br/>
<b>Fermat's Enigma Book (mentioned by Michael)</b>: <a href="https://www.amazon.com/Fermats-Enigma-Greatest-Mathematical-Problem/dp/0385493622/ref=sr_1_1?crid=2X0T5UWJW60Y0&keywords=fermats+enigma&qid=1652154075&sprefix=Fermat%2Caps%2C134&sr=8-1" target="_blank" rel="noopener">amazon.com</a><br/>
<b>Prefect Docs (2.0)</b>: <a href="https://orion-docs.prefect.io" target="_blank" rel="noopener">orion-docs.prefect.io</a><br/>
<b>Prefect source code</b>: <a href="https://github.com/PrefectHQ/prefect" target="_blank" rel="noopener">github.com</a><br/>
<b>A Brief History of Dataflow Automation</b>: <a href="https://www.prefect.io/blog/a-brief-history-of-dataflow-automation/" target="_blank" rel="noopener">prefect.io/blog</a><br/>
<b>Watch this episode on YouTube</b>: <a href="https://www.youtube.com/watch?v=ZcOfetyIrvU" target="_blank" rel="noopener">youtube.com</a><br/>
<b>Episode transcripts</b>: <a href="https://talkpython.fm/episodes/transcript/365/solving-negative-engineering-problems-with-prefect" target="_blank" rel="noopener">talkpython.fm</a><br/>
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