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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-denormalization-in-modern-system-design/
课程评论：没有评论
Coursera 数据反规范化在现代系统设计课程总结 本课程深入探讨了现代数据库和软件工程的前沿领域，重点介绍了传统数据规范化正逐渐被更强大的技术——反规范化视图（denormalized views）所取代。如果您正面临查询缓慢、高延迟或海量用户数据扩展等问题，本课程将教您如何充分利用反规范化视图的潜力。 课程将用简洁易懂的语言剖析复杂概念，展示现代视图如何为特定用例复制和转换数据，减少实时处理需求，并提升整体性能。课程内容涵盖了实现反规范化过程的方法，包括事件驱动架构、批量处理以及物化视图（materialized views）。此外，还将讨论各种反规范化技术，以及如何映射和处理由此带来的众多边缘案例。 本课程旨在挑战我们对数据库和系统设计的传统思维方式。过去，数据规范化（将数据结构化为最小单元，禁止数据重复）是数据库优化的黄金法则。但如今情况已发生变化，在处理海量数据时，规范化反而可能拖慢系统速度。 课程将展示，通过将数据“反规范化”为“反规范化视图”，可以提供一种替代完全规范化的有效解决方案。我们将演示如何以专为快速读取访问设计的、新的反规范化格式复制数据，从而帮助您解决以往因性能原因无法解决的新类型问题。这可能关乎您组织中某个流程因扩展而耗时越长、资源消耗越多，急需重新考虑；也可能是一个团队因缺乏已知解决方案而无法优先处理的新功能请求。无论哪种情况，这种系统设计方法都可能为您的问题提供一种解决方案。 无论您是后端工程师、系统架构师，还是仅仅对数据库性能感到好奇，本系列课程都将为您提供设计更快、更具可扩展性、更可靠系统的知识。您将了解到当今最大的平台（如 Netflix 和 Facebook）如何运用这些原则来处理海量数据，并为用户提供无缝的体验。
We will dive deep into the cutting-edge world of modern database and software engineering, where traditional data normalization is increasingly giving way to more powerful techniques: denormalized views.Whether you're struggling with slow queries, high latency, or scaling data for millions of users, this series will teach you how to harness the true potential of denormalized views.We'll break down complex concepts in simple terms, showing you how modern views replicate and transform data for specific use cases, reduce the need for real-time processing, and improve overall performance. We'll cover how to implement such denormalization process, from using event-driven architecture to using batch processing instead and materialized views. We'll talk about denormalization techniques, and how to map and handle all of the many edge cases they will bring about.In this course, I want to challenge the way we think about databases and system design. In the past, normalization -structuring data into its smallest parts, and forbidding data duplication- was the golden rule for optimizing databases. But nowadays things have changed. When we're dealing with massive amounts of data, oftentimes normalization can actually slow us down.What if there was another approach? I'll show how effective denormalization of your data to what I call "denormalized views" can help provide an alternative solution to complete normalization. We'll demonstrate how replicating your data in a new denormalized format, designed for fast read access for a specific use case, can help you solve a new class of problem you weren't able to serve before because of performance reasons. Perhaps this is about a specific process in your organisation that is taking increasingly longer and more resources to be scaled and that needs to be rethought as soon as possible. Perhaps a new feature request that your team wasn't able to prioritize because there was no known solution to the problem at hand. Either way, this approach to system design is potentially one solution to your problems.Whether you're a backend engineer, system architect, or just someone curious about database performance, this series will arm you with the knowledge to design faster, more scalable, and more reliable systems. Learn how today's largest platforms - from Netflix to Facebook - use these principles to handle massive data volumes and power seamless user experiences.
