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所在平台: Udemy |
课程主页: https://www.udemy.com/course/algorithmic-trading-with-apache-flink-hands-on-guide/
课程评论：没有评论
课程名称：使用 Apache Flink 的算法交易实操指南 课程概述：本课程提供了最新版本的 Apache Flink 所有代码和 API，旨在帮助学员掌握算法交易和实时数据处理的技术。Apache Flink 是近年来在行业中崭露头角的技术，具备超越 Apache Spark 和 Hadoop 的巨大优势。这门综合性的课程面向所有编程经验水平的学员，特别适合数据工程师、软件开发人员、金融分析师以及有志于交易的人员。 课程内容包括从零基础开始的 Apache Flink 概念讲解，直到实时应用的深入实践。每个在项目中使用的 Apache Flink API 和概念都经过详细的实操展示。为学员提供的 Flink 代码和数据集都已附在课程中，以便于学习和实践。此外，所有代码均已更新至最新 Flink 版本。 随着 Apache Flink 在大数据技术领域的迅速崛起，其市场需求也在持续上升。越来越多的企业正在采用 Apache Flink 来处理其实时大数据，未来很可能 Flink 会像 Apache Spark 一样取代 Hadoop，成为新的行业标准。 本课程将帮助您掌握构建一个功能齐全的算法交易系统所需的技能和知识，为您的职业发展打开新机遇。
*** Note: All the codes and APIs are updated with latest Flink version.Apache Flink is the next big thing in the industry with its enormous advantages over Apache Spark and Hadoop. This is the ultimate course on algorithmic trading and real-time data processing with Apache Flink! This course caters for all levels of programming experience. If you're a data engineer, software developer, financial analyst, or an aspiring trader, this comprehensive course is designed to equip you with the skills and knowledge to build a fully functional algorithmic trading system from scratch.With the rate Apache Flink is making waves in the Big data technology space and rapidly gaining momentum in the market, It is assumed that same like Apache Spark replaced Hadoop, Flink can also replace Spark in the nearest future.The demand for Apache Flink in market is already swelling. Many big companies from multiple Industry domains have already started using Apache Flink to process their Real-time Big data and thousands other are diving into.What's included in this course?Complete Apache Flink concepts explained from Scratch to Real-Time implementation.Each and Every Apache Flink APIs and concept used in this project are explained with a HANDS-ON Flink implementation.Flink codes and Datasets used in lectures are attached in the course for your convenience.All the codes are updated with latest Flink version.
