How abstract algebra solves data engineering
Today the story of how twitter engineers came up with a unique solution to data engineering.
Adam interviews Sam about how the abstract algebra and probabilistic data structures help solve fast versus big data issues that many are struggling with.
Adam talks to Sam Ritchie, a machine learning researcher. Stop in to hear Adam and Sam’s conversation about portal abstractions that let you leverage work from other fields.
“You want to go mine the literature of what other people have done. You know you want to go be able to plug these things into your work and really just benefit from this incredible community that’s been cranking for, you know, again, maybe hundreds of years.” – Sam Ritchie
“So I think to go forward like there’s always going to be new discoveries to be made, but one very, very fruitful thing to do. Is to turn around, look back and find these things and say, well, is there an interface I could discover that someone’s already found that would let me just plug into this incredible, almost battery of human creativity that, you know, that just exists waiting for the, taking in maybe dusty old papers and books, but it’s there. No one’s hiding it.” – Sam Ritchie
“I’m aiming to implement these interfaces and pass these tests and then being able to immediately turn around and have like an approximate sliding window counter that would just work with stripes … entire machine learning feature generation interface.” – Sam Ritchie
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