Procella: YouTube's super-system for analytics data storage 4f3a23

06/07/2020

This is a re-release of an episode that originally ran in October 2019. If you’re trying to...

This is a re-release of an episode that originally ran in October 2019. If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this system, the complexity they had to introduce to service all four uses simultaneously, and the impressive engineering that has to go into building something that “just works.”

Understanding Covid-19 transmission: what the data suggests about how the disease spreads +1 año 25:25 The work-from-home episode +1 año 29:06 Putting machine learning into a database +1 año 24:22 Changing our formulation of AI to avoid runaway risks: Interview with Prof. Stuart Russell +1 año 28:58 Keeping ourselves honest when we work with observational healthcare data +1 año 19:08 Ver más en APP Comentarios del episodio 2l1h46