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IoT Data Analytics and Storage


Microsoft

About This Course

Are you ready to help your business begin realizing the business benefits promised by the Internet of Things revolution? Do you want to discover the hidden insights waiting in your business data?

In this course, you will learn how to make the most of your live-stream and historical telemetry data that is being produced by the IoT devices and sensors that support your business.

你准备好帮助企业开始实现物联网革命所承诺的商业利益吗?你想要发现业务数据中隐藏的洞见吗?

在本课程中,您将学习如何充分利用,由支持您业务的物联网设备和传感器生成的实时流和历史遥测数据。

Requirements

Students should understand the following:

  • How IoT is used to achieve business goals
  • How to establish 2-way communication between devices (either real or simulated) and the IoT Hub.

在开始本课程之前,学生应该了解以下内容

  • 物联网如何用于实现业务目标
  • 如何在设备(实际或模拟)和物联网中心之间建立双向通信

What you'll learn

After completing this course, students will be able to:

  • Describe typical telemetry data produced by Azure IoT devices
  • Explain various strategies for analyzing IoT data
  • Explain the differences between warm and cold storage and how each technology is best used
  • Describe how Azure Data Lake can be used for cold storage
  • Explain the process for processing IoT data with IoT Hub, Data Lake Analytics, and Data Lake Storage
  • Understand strategies for querying and analyzing Azure Data Lake data sets
  • Identify the benefits of warm storage
  • Identify operational vs. archive data sets from IoT
  • Provision and configure Azure Cosmos DB
  • Integrate Azure Cosmos DB with Azure Stream Analytics
  • Write IoT data into Cosmos DB as Warm Storage
  • Query Cosmos DB for IoT data
  • Explain the role of IoT Edge devices in analyzing and acting on telemetry data
  • Describe use cases for running analytics on edge devices
  • Modify web-based stream analytics jobs for edge deployment
  • Deploy analytics jobs onto edge devices
  • Deploy other analytics code onto edge devices
  • Combine streaming data with reference data in queries
  • Write queries with different types of time windows
  • Chain together streaming analytics jobs, to allow more sophisticated inputs and outputs
  • Combine warm and cold storage strategies with edge analytics and strategies to quickly react to telemetry data
  • Describe options for performing device management tasks, based on real-time data
  • 描述 Azure IoT 设备生成的典型遥测数据
  • 解释分析物联网数据的各种策略
  • 解释热存储与冷存储之间的差异以及如何最好地使用每种技术
  • 描述 Azure Data Lake 如何用于冷存储
  • 使用 IoT Hub,Data Lake Analytics 和 Data Lake Storage 解释处理物联网数据的过程
  • 了解查询和分析 Azure Data Lake 数据集的策略
  • 确定热储存的好处
  • 识别物联网中的运营与归档数据集
  • 配置和运行 Azure Cosmos DB
  • 整合 Azure Cosmos DB 与 Azure Stream Analytics
  • 将 IoT 数据写入 Cosmos DB 作为热存储
  • 查询物联网数据的 Cosmos DB
  • 解释 IoT Edge 设备在分析和处理遥测数据中的作用
  • 描述在边缘设备上运行分析的案例
  • 修改基于 Web 的流分析作业以进行边缘部署
  • 将分析作业部署到边缘设备上
  • 将其他分析代码部署到边缘设备上
  • 将流数据与查询中的参考数据相结合
  • 使用不同类型的时间窗口编写查询
  • 将流分析工作链接在一起,以实现更复杂的输入和输出
  • 将冷热存储策略与边缘分析和策略相结合,以快速响应遥测数据
  • 描述基于实时数据执行设备管理任务的选项

Course Syllabus

    This course is completely lab-based. There are no lectures or required reading sections. All of the learning content that you will need is embedded directly into the labs, right where and when you need it. Introductions to tools and technologies, references to additional content, video demonstrations, and code explanations are all built into the labs.

    Some assessment questions will be presented during the labs. These questions will help you to prepare for the final assessment.

    The course includes four modules, each of which contains two or more lab activities. The lab outline is provided below.


    Module 1:IoT Analytics and Cold Storage

  • Lab 1:Configuring the Wind Farm Simulator
  • Lab 2:Getting Started with Data Lake Storage and Analytics

  • Module 2:Warm Storage

  • Lab 1:Getting Started with Warm Storage
  • Lab 2:Implementing Business System Integration

  • Module 3:Analytics on the Edge

  • Lab 1:Getting Started with IoT Edge
  • Lab 2:Implementing Analytics on the Edge
  • Lab 3:Deploying an Azure Function to the IoT Edge

  • Module 4:Advanced Analytics

  • Lab 1:Constructing Analytics Queries
  • Lab 2:Managing Analytics Topologies
  • Lab 3:Device Management and Analytics

    本节课全部使用实验教学,不包含任何讲座或阅读内容。所有学习内容都直接嵌入在课程实验中,并已经根据学习进度设计安排顺序,包括对工具和技术的介绍,参考以及额外内容,视频展示预计代码解释。

    测试问题也将出现在学习活动中,这些练习问题将帮助你准备期末考试。

    本节课包含四个单元,每个单元包含至少两个实验练习活动。实验提纲如下。

    第1单元:物联网分析和冷存储

  • 实验1:配置风电场模拟器
  • 实验2:Data Lake存储和分析入门
  • 第2单元:保温储存

  • 实验1:热存储入门
  • 实验2:实现业务系统集成
  • 第3单元:边缘分析

  • 实验1:IoT Edge入门
  • 实验2:在边缘实施分析
  • 实验3:将Azure功能部署到IoT Edge
  • 第4单元:高级分析

  • 实验1:构建分析查询
  • 实验2:管理分析拓扑
  • 实验3:设备管理和分析

Course Staff

Course Staff Chris Howd

Chris Howd

Engineer and Software Developer

Microsoft

Chris is an engineer and software developer who has been working at Microsoft in various roles for the past 15 years. Before coming to Microsoft, Chris worked for the U.S. Department of Defense designing and developing computer controlled instrumentation and robotic systems, and was a self-employed contractor doing engineering research with NASA and select engineering start-ups.

Course Staff Rob Collins

Rob Collins

Founder and lead consultant

RCP Consultants

Rob Collins is founder and lead consultant at RCP Consultants. He has been working with C# and the .NET Framework since its initial release more than fifteen years ago. He has been delivering software for enterprise customers, the mass market retail chain, mid-market companies, and startups for more than twenty years.

Frequently Asked Questions

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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