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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learn-by-example-apache-flink/
课程评论：没有评论
课程名称：通过实例学习：Apache Flink 课程概述：Flink 是一种流处理技术，除了流处理外，还具备批处理、图算法和机器学习等多种功能。通过使用 Flink，您可以构建需要快速响应最新数据的应用，例如监测支付网关故障的峰值或根据实时股价波动触发交易。本课程提供了 30 个解决方案示例，旨在帮助学员构建适用于流处理和批处理的 Flink 应用。 课程内容包括： 1) 数据流 API 的转换：过滤、映射、平坦映射和归约 2) 多个流上的操作：联合、协作、连接、共同映射、连接和迭代 3) 窗口操作：翻滚窗口、滑动窗口、计数窗口和会话窗口；时间的概念以及如何实现自定义窗口函数 4) 利用状态和检查点管理故障容错 5) 数据集 API 的转换：过滤、映射、归约和归约分组 6) 使用 Flink-ML 动态应用机器学习算法 7) 使用 Gelly 表示图数据 本课程将为对实时数据处理和 Flink 框架感兴趣的学员提供深入的实践经验和理论知识。
Flink is a stream processing technology with added capability to do lots of other things like batch processing, graph algorithms, machine learning etc. Using Flink you can build applications which need you to be highly responsive to the latest data such as monitoring spikes in payment gateway failures or triggering trades based on live stock price movements. This course has 30 Solved Examples on building Flink Applications for both Streaming and Batch Processing What's covered? 1) Transformations in the DataStream API: filter, map, flatMap and reduce 2) Operations on multiple streams: union, cogroup, connect, comap, join and iterate 3) Window operations: Tumbling, Sliding, Count and Session windows; the notion of time and how to implement custom Window functions 4) Managing fault-tolerance with State and Checkpointing 5) Transformations in the DataSet API: filter, map, reduce, reduceGroup 6) Applying ML algorithms on the fly using Flink-ML 7) Representing Graph data using Gelly
