A Practical Introduction to Stream Processing培训

课程编码

introtostreamprocessing

课程时长

21 小时 通常来说是3天,包括中间休息。

要求

  • Programming experience in any language
  • An understanding of Big Data concepts (Hadoop, etc.)

课程概览

Stream Processing指的是“运动中的数据”的实时处理,即,在接收数据时对数据执行计算。这些数据被读取为来自数据源的连续流,例如传感器事件,网站用户活动,金融交易,信用卡刷卡,点击流等。 Stream Processing框架能够读取大量传入数据并几乎立即提供有价值的见解。

在这个以讲师为主导的现场培训(现场或远程)中,参与者将学习如何使用现有的大数据存储系统和相关的软件应用程序和微服务来设置和集成不同的Stream Processing框架。

在培训结束时,参与者将能够:

  • 安装和配置不同的Stream Processing框架,例如Spark Streaming和Kafka Streaming。
  • 理解并选择最合适的工作框架。
  • 数据处理连续,同时,并以逐个记录的方式进行。
  • Stream Processing解决方案与现有数据库,数据仓库,数据湖等集成。
  • 将最合适的流处理库与企业应用程序和微服务集成在一起。

听众

  • 开发商
  • 软件架构师

课程 格式

  • 部分讲座,部分讨论,练习和繁重的实践练习

笔记

  • 要申请本课程的定制培训,请联系我们安排。

Machine Translated

课程大纲

Introduction

  • Stream processing vs batch processing
  • Analytics-focused stream processing

Overview Frameworks and Programming Languages

  • Spark Streaming (Scala)
  • Kafka Streaming (Java)
  • Flink
  • Storm

Comparison of Features and Strengths of Each Framework

Overview of Data Sources

  • Live data as a series of events over time
  • Historical data sources

Deployment Options

  • In the cloud (AWS, etc.)
  • On premise (private cloud, etc.)

Setting up the Development Environment

Installing and Configuring

Assessing Your Data Analysis Needs

Integrating the Streaming Framework with Big Data Tools

Transforming the Input Data

Inspecting the Output Data

Integrating the Stream Processing Framework with Existing Applications and Microservices

Troubleshooting

Summary and Conclusion

客户评论

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