What is a Multi-armed Bandit Experiment?
Let’s imagine you’ve decided to hit up the casino and try your luck at the slots. You don’t know which slots are best, but you have reason to believe that some of the slot machines are better than others. You only have a limited amount of money to play with, so you want to play each of the machines enough to learn which is going to be best (explore), and then as you learn, play the best machines most frequently to maximize your return (exploit). Choosing which machine to play is referred to as the exploration / exploitation dilemma.
Oftentimes database tables begin with the best of intentions. They can start out narrow and clearly named with well-defined content, but as our applications and teams grow, we may start to notice that those same tables begin to take on a different shape. It’s possible that they grow wider and are harder to work with than you remember. They could be holding data around multiple concepts, which might mean your models are breaking the single responsibility principle. Perhaps that table didn’t start out concise at all. There are many common scenarios that cause tables to exist in a cumbersome form, but it doesn’t mean they have to stay that way.
Charting a Path from Redshift to Snowflake
ezCater is a data-driven company. For a long time, the principal source for our metrics was our Redshift data warehouse. As a result, we invested heavily in building out the data systems that fed Redshift. We also staffed up a team of engineers dedicated to building, enhancing, and growing our data systems. (And yes, we're hiring!)