February 2022
Intermediate to advanced
360 pages
11h 47m
Polish
Content preview from Spark
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Rozdział 9. Tworzenie niezawodnych jezior danych za pomocą Apache Spark
W poprzednich rozdziałach wyjaśniliśmy, jak można łatwo i efektywnie używać frameworka Apache Spark do tworzenia skalowanych i wydajnych potoków przetwarzania danych. Jednak w praktyce wyrażenie logiki przetwarzania powoduje rozwiązanie tylko połowy problemu związanego z tworzeniem potoku. Dla inżyniera danych, naukowca lub analityka danych ostatecznym celem tworzenia potoków jest wykonywanie zapytań do przetwarzanych danych i otrzymywanie na ich podstawie pewnych informacji. Wybór rozwiązania w zakresie pamięci masowej decyduje o kompleksowej (np. od niezmodyfikowanych danych do wniosków na ich podstawie) niezawodności i wydajności działania potoku danych.
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.