AWS re:Invent 2018: Big Data Analytics Architectural Patterns & Best Practices (ANT201-R1)
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29.1
Amazon Web Services771 тыс
Следующее
27.11.18 – 2 0041:02:00Популярные
9 дней – 39418:17Опубликовано 27 ноября 2018, 19:12
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Complete Title: AWS re:Invent 2018: [REPEAT 1] Big Data Analytics Architectural Patterns & Best Practices (ANT201-R1)
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Complete Title: AWS re:Invent 2018: [REPEAT 1] Big Data Analytics Architectural Patterns & Best Practices (ANT201-R1)
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