mycelium
Joined 11 years ago
Joined 11 years ago
I would say that you should pilot with a few ETL vendors. We currently use Fivetran, they're fine but we've had enough burps that I cannot cold recommend them over other vendors. I cannot for the life of me remember the details, but I think we went with them over Stitch for pricing reasons.
If people can't get off their asses to stop working with a company that has worked with organizations responsible for thousands of deaths, I don't think support of Gab is going to drive anyone's payment platform decisions.
Thinking of it as inspection alone is a disservice to the cultural value of code review. You want a process that can teach a team member to contribute with lesser inspection? Code review.
I've typically found that with a new team or team member, initially there are a lot of patches returned for modification. After 3-9 months, it tends towards 80% of patches being one-shot LGTMs, the remaining 20% having spec issues or substantive style issues (ie. this module structure will bite us in the butt because...)
This idea of communicating shared knowledge also points at more efficient ways to do that, if CR is a bottleneck:
- Technical onboarding - Google does a great job of this. Taking time to explain how to work with your technologies, and what the expected code style is.
- Linting + style guides - Arguing over style is dumb.
Beyond the "this will likely deadlock without a timeout", "this does not clean up after itself on exception", "if the cache is not hot, this operation you assume is 1 sec will take 1 min" type thing — one of the strongest points of code review is having a conversation about "this code is hard to understand" and turning code that's correct if you think about it really hard into obviously correct code.
It takes a reasonably strong reviewer to get there though, and that's not always possible.
Draw from that what you may, but I bet that dynamic is happening in other places.
I'd be more concerned in Google's case about "legitimate" but still problematic uses of data: disclosure to law enforcement, crazy specific ad targeting, machine learning for military applications, etc.
I'm going to echo my sibling commenter, would love to hear it if you have any pointers on where to start reading.
I'm a software engineer but data work comes up all the damn time. Seeing coworkers approach causality in a hand-wavy way without knowing a more rigorous approach to suggest is frustrating! I had hoped Pearl's book was exactly that.
http://discovermagazine.com/1987/may/02-the-worst-mistake-in...