• 9 Posts
  • 19 Comments
Joined 1 year ago
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Cake day: June 27th, 2023

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  • I haven’t used them in Spark directly but here’s how they are used for computing sparse joins in a similar data processing framework:

    Let’s say you want to join some data “tables” A and B. When B has many more unique keys than are present in A, computing “A inner join B” would require lots of shuffling if B, including those extra keys.

    Knowing this, you can add a step before the join to compute a bloom filter of the keys in A, then apply the filter to B. Now the join from A to B-filtered only considers relevant keys from B, hopefully now with much less total computation than the original join.

















  • the author is more interested in how humanity as a whole would react to his fictional scenario than he is with writing characters with depth

    This was my impression as well and I think it works only because the fictional scenarios are extremely creative along with sometimes gratuitous science-fiction details from the author’s imagination. And even though most characters seemed unrealistic as people I still liked them as characters and found them memorable.

    I also read (listened to) Voyagers by Ben Bova recently and while the fictional scenario was interesting, the character development leaned heavily on the relationship between the hero scientist and the promiscuous young scientist, a writing style which I found more boring.




  • I agree with how you characterized it and the term “ai engineer” didn’t resonate with me as defined by the author. If such an engineer doesn’t need to know about the data involved (“nor do they know the difference between a Data Lake or Data Warehouse”) then I don’t think they will be able to ship an AI/ML product based on data.

    New titles can be helpful for sorting out different roles with some shared skillsets such as the distinction which emerged between Data Scientist and ML Engineer at some companies to focus the latter on shipping production software using ML.