Engineering orgs have two stake holders to satisfy: you want happy customers, who pay the bills, and happy engineers, who should be freed from too much toil and tech debt. @lizthegrey talking us thru the o11y maturity model
Observability is critical to high performing teams over the entire software development life cycle.
🐝 operational resilience
🐝 quality code
🐝 predictable releases
🐝 managing complexity and tech debt
🐝 user insights
excited to see what the discussion groups for each subject area come up with 😍🐝
Side note, I heard @kelescopic refer to these as "reverse panels" and I am charmed by that idea. I always feel like panels are a waste of time; like, most people's advice isn't that interesting, or so vague as to be practically useless.
Nobody is gonna give real talk in public.
I have always said I would be way more interested in discussing the topic with five members of the audience drawn at random, than listening to a panel of self-styled experts. And that's effectively what this is!
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The question is, how can you interview and screen for engineers who care about the business and want to help build it, engineers who respect sales, marketing and other functions as their peers and equals?
It's a great question!! I have ideas, but would love to hear from others.
I said "question", but there are actually two: 1) how to hire engineers who are motivated by solving business problems and 2) aren't engineering supremacists.
Pro tip: any time you see someone confidently opining on what all good CTOs know or do, it is ✨bullshit✨
There is no stock template for CTO, or default set of expectations or responsibilities. It stands alone among the C-levels in that good ones are all over the freaking map.
This may not hold true for publicly traded companies. But in my experience, a good CTO can be:
* over all of R&D
* over engineering, like a VP eng
* like a principal eng or architect
* team lead for special projects
* a great senior programmer
(continued... 👉)
A CTO can also be:
* a great communicator and popularizer
* on the road as a devrel
* a field CTO, whose authority opens doors to big customers
* a product visionary who sweats the details
* more of a cofounder than technical contributor, sharing "company-running" duties w/CEO
Yeah, this is a fair caveat. If you're already a decent senior engineer and manager, it's kind of possible to split your attention between managing a small team and writing code.
But you aren't going to improve at either skill set. Those cycles get devoured by context switching.
Tech lead managers ("TLMs") are a mistake we make over and over in this industry. I've written about this a bit, but the definitive post was written by @Lethain.
My coworker @suchwinston wrote a terrific piece on burnout before the break:
There's a reason why burnout and work/life balance are such evergreen topics, and it's not actually because the world is so terribly harsh and everyone is criminally overworked.honeycomb.io/blog/product-m…
Just to be clear: some places *are* awful, and some people *are* criminally overworked. But burnout and work/life balance are an issue for everyone, not just those people.
I think this is bc there is no real "solution". Each of us has to find and maintain our own equilibrium.
It takes a lot of hard work to become good at technology, and a lot more hard work to maintain your edge in a fast-changing industry.
I don't know of anyone for whom this is _easy_. None of this is remotely natural, from an evolutionary perspective. 🐒
This is an astute point. For all the ink that has been spilled about what observability is or is not, or how generation 2.0 differs from 1.0, it's actually quite simple.
"Observability" was coined to denote the emergence of ✨high cardinality✨ support in telemetry and tooling.
Cardinality, for those new to my feed 🤣 refers to the number of unique items in a set. Gender drop-down with three options? Low cardinality. Gender field you can write to? Much, much higher cardinality.
Metrics can't do high cardinality data. A metric can only be a number.
Logs *can* handle high cardinality data, which is why logs have always been so much more powerful than metrics.
The most useful debugging data is always the high cardinality shit. Request IDs, uncommon strings, whatever. It reduces the search space fast.