Everything You Need to Know About Big Data: From Architectural Principles to Best Practices
In this session, we discuss architectural principles that help simplify big data analytics. We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll discuss 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.
Видео Everything You Need to Know About Big Data: From Architectural Principles to Best Practices канала Amazon Web Services
Видео Everything You Need to Know About Big Data: From Architectural Principles to Best Practices канала Amazon Web Services
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Delivering High Quality Analytics at NetflixWhat is the difference between Database vs. Data lake vs. Warehouse?Hadoop Tutorial For Beginners | Hadoop Ecosystem Explained in 20 min! - Frank KaneKenneth Cukier: Big data is better dataFunctional Data Engineering - A Set of Best Practices | LyftEverything You Ever Wanted To Know About Diesel Engines Motorz #75AWS re:Invent 2018: Big Data Analytics Architectural Patterns & Best Practices (ANT201-R1)Future of Data EngineeringBig Data Architecture Patternsyou need to learn AWS RIGHT NOW!! (Amazon Web Services)LOTUS - Everything You Need to Know | Up To SpeedEnterprise Data Lake: Architecture Using Big Data Technologies - Bhushan Satpute, Solution ArchitectData Architecture 101 for Your BusinessData Engineering Principles - Build frameworks not pipelines - Gatis SejaBest Practices Working with Billion-row Tables in Databases180. Solar 101 - everything you need to know about going solar!Big Data Tutorial For Beginners | What Is Big Data | Big Data Tutorial | Hadoop Training | EdurekaAWS re:Invent 2020: An introduction to data lakes and analytics on AWSEverything You NEED to know about 808sAWS re:Invent 2018: Effective Data Lakes: Challenges and Design Patterns (ANT316)