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所在平台: Udemy |
课程主页: https://www.udemy.com/course/apache-flink-relational-programming-using-table-api-and-sql/
课程评论：没有评论
课程名称：使用Table API和SQL进行Apache Flink关系编程 课程概述： Apache Flink因其在处理状态计算方面的先进能力而日益受到欢迎，能够满足高吞吐量和高性能用例的需求。本课程将帮助学生掌握Apache Flink的强大功能，这是一个现代的分布式计算框架，提供统一的批处理和流处理数据工作负载的方法。课程专注于通过Apache Flink的Table API和SQL接口（示例使用Python）来展示关系编程范式，提供直观而强大的抽象方法，以处理来自有界（批处理）或无界（流处理）源的海量数据。 学生将通过多个实例学习如何使用Flink进行批处理，具体涵盖从文件系统中以CSV格式消费、处理和生成结果的过程。此外，还将通过若干示例学习如何在本地Docker化的Kafka集群中使用Flink进行流处理，涉及消费、处理和生成结果。 Apache Flink支持使用Table API和SQL接口在Java、Scala和Python中开发应用程序，但本课程专注于使用Apache Flink的Python绑定。这一选择主要是考虑到Python在大数据工程生态系统中的受欢迎程度，以及在现有Apache Flink课程中对Python的覆盖不足，现有课程主要集中于Java和Scala API。 总之，本课程将为希望掌握Apache Flink及其在批处理和流处理中的应用的学习者提供实用的技能。
Apache Flink is widely growing in popularity for its ability to perform advanced stateful computations in a way that scales to meet the demands of both high throughput and high performance use cases. Not only is Apache Flink very scalable and performant it also integrates with a wide variety of source and sink data systems like flat files (CSV,TXT,TSV), Databases, and Message Queues (Kafka, AWS Kinesis, GCP Pub/Sub, RabbitMQ). In this course students will learn to harness the power of Apache Flink which is a modern distributed computing framework providing a unified approach to both batch and streaming data processing workloads. This course specifically focuses on the relational programming paradigm exposed through Apache Flink's Table API and SQL interface (with examples in Python) offering intuitive yet powerful abstractions to process vast amounts of data in either bounded (batch) or unbounded (streaming) sources.Students learn batch processing with Flink through many examples of consuming, processing, and producing results from/to the filesystem in CSV format.Students also learn stream processing with Flink through several examples consuming, processing and producing results from/to Apache Kafka running in a local Dockerized Kafka cluster.Apache Flink offers support for developing Flink applications with the Table API and SQL interface in Java, Scala and Python. However, this course focuses on using the Python bindings for Apache Flink. The focus on Python for this course was chosen due to the popularity of the Python programming language, particularly in the big data engineering ecosystem, but also due to the underrepresentation of Python in existing Apache Flink courses which primarily cover the Java and Scala APIs of Flink.
