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所在平台: Udemy |
课程主页: https://www.udemy.com/course/spark-structured-streaming-30-all-you-need-to-know/
课程评论：没有评论
课程名称：Spark结构化流处理3.0：您需要了解的一切 课程概述：在许多行业中，快速获取数据的行动是必要的，而流处理正是实现这一目标的关键。然而，流处理也伴随着一系列理论、挑战和最佳实践。Apache Spark在流处理方面得到了巨大的发展。Spark结构化流处理的丰富特性引入了一定的学习曲线，本课程旨在以友好且易于理解的方式呈现这些概念。结构化流处理是一个可扩展和容错的流处理引擎，构建在Spark SQL引擎之上。您可以以与处理静态数据相同的方式表达流计算，Spark SQL引擎将负责增量和持续地运行它，并在流数据持续到达时更新最终结果。该技术使数据工程师和数据科学家能够处理来自各种源（包括Kafka、Flume和Amazon Kinesis等）的实时数据。 本课程将帮助您建立基础知识。您将学习批处理和流处理之间的区别、编程模型、API及流处理特有的挑战。课程将通过丰富的实例和动手操作，快速引导您理解流处理的概念，深入探讨其内部工作原理，并在课程结束时设计一个用例。所有活动将在云端使用Spark 3.0进行。
Getting faster action from the data is the need of many industries and Stream Processing helps doing just that. But it comes with its own set of theories, challenges and best practices.Apache Spark has seen tremendous development being in stream processing. The rich features of Spark Structured Streaming introduces a learning curve and this course is aimed at bringing all those concepts in a friendly and easy to reflect manner. Structured Streaming is a scalable and fault-tolerant stream processing engine built on the Spark SQL engine. You can express your streaming computation the same way you would express a batch computation on static data. The Spark SQL engine will take care of running it incrementally and continuously and updating the final result as streaming data continues to arrive. It allows data engineers and data scientists to process real-time data from various sources including (but not limited to) Kafka, Flume, and Amazon Kinesis.This illustrative course will build your foundational knowledge. You will learn the differences between batch & stream processing, programming model, the APIs and the challenges specific to stream processing. Quickly we'll move to understand the concepts of stream processing with wide varieties of examples & hands-on, dealing with inner working and taking a use case towards the end. All of this activity will be on cloud using Spark 3.0.
