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所在平台: Udemy |
课程主页: https://www.udemy.com/course/stream-processing-frameworks-for-big-data-the-internals/
课程评论：没有评论
课程名称：大数据流处理框架的内部机制 课程概述：你是否需要在下一个项目中使用流处理却不知从何开始？或者你想向数据工程师角色发展，开始建立关于流处理的知识？本课程为你详细解析并比较几种流行的流处理框架，帮助你为你的用例选择合适的框架并开启学习之旅。我们将重点介绍Flink、Kafka Streams、Spark Streaming和Structured Streaming，这四个框架是当前行业的最新技术。你将理解它们的特点、特性和差异。这门课程是学习有关这些框架API和编程语言的完美入门。 课程涵盖的相关内容包括： - 一般特性 - API - 延迟和吞吐量性能 - 可扩展性 - 弹性 - 故障容忍 - 状态管理 - 部署等 我们将深入探讨不同机制和方法的工作原理以及其优缺点。需要注意的是，本课程并非编程课程，更注重理论方面的内容。课程结束时，你将获得一个简明的概述，回顾所学知识。本课程的内容基于Giselle的博士研究成果，她对这些框架在各个特性方面进行了基准测试和分析。
Do you need to use stream processing for your next project but have no idea where to begin? Or do you want to grow into a data engineering role and want to start building up knowledge on stream processing?In this course, we give a detailed explanation and comparison of several popular stream processing frameworks. At the finish line, you will be able to make a well-grounded selection of the right framework for your use case or to start your learning process. We will cover Flink, Kafka Streams, Spark Streaming and Structured Streaming. These are the four frameworks that are currently the state-of-the-art in the industry.You will understand their features, characteristics and differences. This course gives you the perfect primer to start learning and better understand the APIs and programming languages behind these frameworks.This course covers all relevant aspects: - their general characteristics- APIs- latency and throughput performance- scalability- elasticity- fault tolerance- state management- deployment-...We will dive deeply into the workings and the advantages and disadvantages of the different mechanisms and approaches.!!! This course is not a programming course but focuses on more theoretical aspects. At the end, you will be provided with a concise overview on what was covered. The content of this course is based on the results of Giselle's PhD work in which she benchmarked and analyzed these frameworks on all these characteristics.
