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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/serverless-data-processing-with-dataflow-foundations
课程评论：没有评论
课程名称：无服务器数据处理与Dataflow：基础 课程概述：本课程是“无服务器数据处理与Dataflow”系列的第一部分，共有三门课程。在本课程中，我们首先回顾Apache Beam及其与Dataflow的关系。接着，我们讨论Apache Beam的愿景以及Beam可移植性框架的优点。Beam可移植性框架实现了开发者可以使用他们喜欢的编程语言与首选执行后端的愿景。随后，我们展示了Dataflow如何帮助您分离计算和存储，同时节省成本，以及身份、访问和管理工具如何与您的Dataflow管道交互。最后，我们探讨了如何在Dataflow上为您的用例实施合适的安全模型。 先决条件：该课程系列建立在数据工程专门化中涵盖的概念之上。我们推荐以下先决条件课程： (i) 在Google Cloud上构建批处理数据管道：涵盖Dataflow的核心原则 (ii) 在Google Cloud上构建弹性流分析系统：涵盖流媒体的基本概念，如窗口、触发器和水印 课程大纲： - 模块名称：引言 描述：本模块涵盖课程大纲，并快速回顾Apache Beam编程模型和Google的Dataflow托管服务。 - 模块名称：Beam可移植性 描述：在此模块中，我们将学习四个部分：Beam可移植性、Runner v2、容器环境和跨语言转换。 - 模块名称：使用Dataflow分离计算和存储 描述：在此模块中，我们讨论如何通过Dataflow分离计算和存储。本模块包含四个部分：Dataflow、Dataflow Shuffle服务、Dataflow流引擎和灵活资源调度。 - 模块名称：IAM、配额和权限 描述：在此模块中，我们讨论运行Dataflow所需的不同IAM角色、配额和权限。 - 模块名称：安全性 描述：在此模块中，我们将探讨如何为您的用例在Dataflow上实施合适的安全模型。 - 模块名称：总结 描述：在本课程中，我们首先回顾了Apache Beam及其与Dataflow的关系。
Name:Introduction
Description:This module covers the course outline and does a quick refresh on the Apache Beam programming model and Google’s Dataflow managed service.
Name:Beam Portability
Description:In this module we are going to learn about four sections, Beam Portablity, Runner v2, Container Environments, and Cross-Language Transforms.
Name:Separating Compute and Storage with Dataflow
Description:In this module we discuss how to separate compute and storage with Dataflow. This module contains four sections Dataflow, Dataflow Shuffle Service, Dataflow Streaming Engine, Flexible Resource Scheduling.
Name:IAM, Quotas, and Permissions
Description:In this module, we talk about the different IAM roles, quotas, and permissions required to run Dataflow
Name:Security
Description:In this module, we will look at how to implement the right security model for your use case on Dataflow.
Name:Summary
Description:In this course, we started with the refresher of what Apache Beam is, and its relationship with Dataflow.
This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow. Prerequisites: The Serverless Data Processing with Dataflow course series builds on the concepts covered in the Data Engineering specialization. We recommend the following prerequisite courses: (i)Building batch data pipelines on Google Cloud : covers core Dataflow principles (ii)Building Resilient Streaming Analytics Systems on Google Cloud : covers streaming basics concepts like windowing, triggers, and watermarks >>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
