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所在平台: Udemy |
课程主页: https://www.udemy.com/course/apache-flink-with-scala-3/
课程评论：没有评论
课程名称：Apache Flink与Scala 3 课程概述：本课程是2024年发布的最新Flink与Scala 3的课程，涵盖Flink的基本和高级概念，并配有实践练习。课程深入剖析了将最新Flink版本1.17与Scala 3及Kubernetes集成的复杂性。自Flink 1.15版本起，移除了对Scala的支持，以便更容易地与多个Scala版本（包括Scala 3）集成。课程包括逐步的视频演示，解决在Kubernetes上部署Scala 3中的Flink应用程序时的类路径冲突问题。 学习内容： 1. **Flink与流处理基础**：理解Flink如何处理流处理、分布式和有状态计算；掌握Flink的架构及其组件；学习如何部署和管理Flink应用程序的生命周期。 2. **Flink数据管道**：理解开发流应用程序的不同抽象层次；掌握包括数据摄取、有效数据转换及使用低级API控制应用程序等基本概念，能够以各种方式实时处理大数据，并将输出流发送到数据接收端。 3. **与Apache Kafka的集成**：学习Kafka源和Kafka接收器的配置；掌握如何在built.sbt中设置Kafka依赖并将Kafka作为数据源或接收器与Flink集成。 4. **时间处理、水印和窗口**：能够使用Flink的水印机制和窗口操作处理事件时间，包括翻滚窗口、滑动窗口和全局窗口。 5. **容错机制**：学习使用Flink的关键概念（checkpoint机制、map state、list state和value state）编写有状态的应用程序。 6. **与Kubernetes的集成**：学习Flink的部署模式，并通过视频演示逐步部署自己的Flink应用程序，掌握所有部署步骤和配置。 课程收益： - 30多个Flink视频课程，配有幻灯片和示意图，以及访问包含所有代码的Github仓库。 - 通过编写20多个常见用例的Flink应用程序进行实践，跟随动手视频课程学习。 - 快速掌握Flink 1.17的所有依赖配置及在Kubernetes上部署应用程序的步骤。 - 学习可转移的大数据流处理和分布式系统的原则，可应用于其他流处理系统。
As the latest Flink course in Scala 3 released in 2024, it covers Flink's basic and advanced concepts each followed by hands-on exercises. This course cuts through all the complexities of integrating the very recent Flink version 1.17 with Scala 3 and Kubernetes. Starting with version 1.15 of Flink, Scala support was removed to make it easier to integrate with multiple versions of Scala, including Scala 3. This course includes step by step video demonstrations on how to resolve the problem of classpath collision when deploying Flink applications in Scala 3 on Kubernetes.Concretely, here's what you will learn to either ace your next Flink interview or be ready to write your production level Flink application:1. Flink and Streaming FundamentalsYou'll understand how Flink handles stream processing, distributed and stateful computation; You'll learn Flink's architecture including Flink cluster's components; You'll know how to deploy and manage the lifecycle of a Flink application.2. Flink Data PipelineYou'll understand different levels of abstraction for developing streaming applications; You'll be able to process big data in real time any way you want to by mastering fundamental Flink concepts including: data ingestion, efficient data transformation, controlling your applications with lower level APIs, producing output streams to data sinks.3. Integration with Apache KafkaYou'll learn configuration of Kafka Source and Kafka Sink; You'll master how to set up Kafka dependencies in built.sbt and how to integrate Kafka with Flink as a data source or data sink.4. Time Handling, Watermarks and WindowsYou'll be able to handle event time processing using Flink's watermarks mechanism and window operation including tumbling window, sliding window and global window.5. Fault TolerantYou'll be able to write stateful applications using Flink's key concepts including checkpoint mechanism, map state, list state and value state.6. Integration with KubernetesYou'll learn Flink's deployment modes and deploy your own Flink application on Kubernetes by following along the video demonstration of every deployment step and deployment configurations.What you'll get from this course:You will get 30+ total Flink video lessons with slides and illustrative diagrams plus access to Github Repo with all the code in the course;You can practice by writing more than 20 Flink applications for common use cases and following along the hands-on video lessons;You will quickly master the configurations of all the dependencies and steps to deploy Flink 1.17 applications on Kubernetes by following the video demonstration;You will learn transferrable principles of big data streaming and distributed systems that you can apply on other streaming systems.
