- 具有 Python 和 Apache Kafka 的经验
- 熟悉流处理平台
观众
- 数据工程师
- 数据科学家
- 程序员
Apache Spark 流式处理是一种可扩展的开源流 处理系统，允许用户处理来自受支持源的实时数据。Spark Streaming 支持对数据流进行容错处理。
这种以讲师为主导的现场培训（现场或远程）针对希望使用Spark Streaming功能处理和分析实时数据的数据工程师，数据科学家和程序员。
在本次培训结束时，参与者将能够使用 Spark Streaming 处理实时数据流，以便在数据库、文件系统和实时仪表板中使用。
课程形式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在现场实验室环境中实际实施。
课程定制选项
- 如需申请此课程的定制培训，请联系我们进行安排。
介绍
Spark Streaming 功能和体系结构概述
- 支持的数据源
- 核心 API
准备环境
- 依赖
- Spark 和流式处理上下文
- 连接到 Kafka
处理消息
- 将入站消息解析为 JSON
- ETL 过程
- 启动流式处理上下文
执行窗口化 Stream Processing
- 滑动间隔
- 检查点传送配置
- 启动环境
对处理代码进行原型设计
- 连接到 Kafka 主题
- 使用 Paw 从数据源检索 JSON
- 变更和额外处理
流式传输代码
- 作业控制变量
- 定义要匹配的值
- 功能和条件
获取流输出
- 计数器
- Kafka 输出（匹配和不匹配）
故障 排除
总结和结论
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