微软Azure IoT Suite：实现实时物联网数据分析与实战指南

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"Apress.IoT.Solutions.in.Microsofts.Azure.IoT.Suite" 是一本深入探讨如何在Microsoft Azure IoT Suite平台上收集和分析物联网(IoT)设备数据的专业书籍。作者Scott Klein引导读者了解在这个云基础架构中实现数据采集与实时分析的过程。书中内容涵盖了实际操作中的数据获取策略，以及如何利用这些数据来提升用户体验和进行预测性分析，以便发现可能带来商业价值的异常模式。该书的主题围绕着Microsoft Azure IoT Suite在物联网时代的角色，重点在于帮助开发者和企业构建适应连接设备的解决方案。读者可以从中学习到如何有效地将日常设备如智能灯泡、恒温器，甚至语音控制设备（如Google Home和Amazon Alexa）连接到云端，从而获取海量数据。通过实例演示，作者详细展示了如何运用数据分析技术，对这些设备产生的数据进行实时监控和解析，以便于识别出潜在的问题或机会。本书不仅介绍了理论概念，还提供了实践指导，使读者能够掌握在现实世界中设计和实施IoT数据采集和分析系统的方法。对于那些希望在物联网领域扩展业务、提升设备性能和用户体验的企业，或者寻求利用Azure IoT Suite技术进行创新的开发者来说，这本书是一本不可或缺的参考和实践指南。版权信息表明，本书受版权保护，所有权利归作者Scott Klein所有。读者在使用内容时需尊重版权规定，不得未经许可进行翻译、复制、广播或任何形式的信息存储和检索。同时，书中提及的商标、标志和图像均为相关企业的知识产权。 "Apress.IoT.Solutions.in.Microsofts.Azure.IoT.Suite" 是一本实用的教程，旨在帮助读者掌握物联网设备数据处理的核心技术和应用策略，确保在云计算环境下高效地管理和分析这些设备的数据，为业务增长和用户优化提供有力支持。

xix

Introduction

I’ll cut right to the chase here and not be long-winded. The intent of this book is to provide some insight into

how Microsoft’s Internet of Things and Intelligence and Analytics services can be used together to build an

end-to-end solution. This book takes one example and walks that example through the book, implementing

service after service, to help stitch together the end-to-end picture. Along the way, the book will stop and look

at other interesting, real-world scenarios to help clarify and broaden the picture, to give you further ideas.

This book does not go deeply into any specific service. Entire books could probably be written on each

service covered in this book. That is not the intent of this book. My goal is to discuss each service enough to

help you decide how you can use the particular service in an IoT solution. I want to get you excited about

working with IoT, its capabilities and possibilities. This area is still in its infancy, and there is so much more

that can be done to make the quality of our lives better—not to the WALL-E point because heaven forbid we

ever get to that point, but to where we are using IoT to save lives, make things safer, and do great things for

this planet and humankind.

I tried my best to keep the screenshots and related information up to date. However, if you work with

Microsoft Azure, and probably in cloud service for that matter, you know how fast things can change. If the

screenshots changed between when I wrote the chapter and it got printed and into your hands, that’s the

way working with cloud services is. It should be close enough for you to figure out the differences and work

with them.

Almost every author will tell you that when they write a book, they look back and wish they could have

added “x” or talked more about “y.” Such is the case in this book as well. Not to make excuses, but I had to

make decisions on many areas just to get the book out. I couldn’t keep holding the book up because feature

“x” was coming or improvement “y” was about to be released. Thus, I relied on feedback and input to create

what is in your hands. I hope you find it good enough.

There are many things I would have liked to discuss in more detail. For example, I would have liked to

discuss more about Azure Stream Analytics sliding windows and spent more time in U-SQL (which probably

deserves its own book, by the way). However, to get the book out, I hopefully provided enough information

to paint the end-to-end picture I was striving for.

If you want to learn more about these amazing services, especially feature “x” and improvement “y,”

I’ll continue to add to this book via my blog, http://aka.ms/SQLScott. If you have ideas or want more

information on a specific topic, feel free to ping me via Twitter at @SQLScott or my blog. I am always

interested in your questions and feedback because that makes us both better.

S. Klein, IoT Solutions in Microsoft’s Azure IoT Suite, DOI 10.1007/978-1-4842-2143-3_1

CHAPTER 1

The World of Big Data and IoT

If you’re reading this book, I would venture to guess it is for one of two primary reasons. First, you want to

learn and get into the big data/Internet-of-things space and technologies. Second, you need to learn about

dealing with big data. The first is more from the perspective of curiosity and career growth and you are to be

applauded for taking the initiative and first step into the fantastic world of dealing with vast amounts of data.

If it’s the second, I pray for you because this is a “Holy data explosion, Batman! I need to make sense of all

this data!” situation and you have some work ahead of you.

Now, I don’t want to scare you into thinking that dealing with big data is scary and overwhelming, so

don’t hyperventilate. It can certainly appear overwhelming, especially if you look at all the technologies that

have emerged over the last few years to deal the exponential growth of data and help solve the data insights

problems.

But before we begin the journey into this wonderful space, let’s go back in time and look at where things

have come from, sort of a “history of data.” It wasn’t too long ago that data was typically stored in simple text

format in flat files in either a comma- or tab-delimited format. Reading and writing data took a fair amount

of code. If you’re over 40, you remember these days not too fondly. Luckily, the relational database came to

the rescue, which made dealing with data much easier. Gone were the days of parsing files and reading one

line at time; instead we could write SQL queries and have the processing done more efficiently at the server.

The relational database veiled the complexity of the storage layer while at the same time provided built-in

relational capabilities that flat files did not.

The fact that relational databases still have value stands as a tribute to their effectiveness and

capabilities even when storing and working with terabytes of data. But we need to be realistic. As good

and effective as a relational database is, we need to have a clear understanding that today’s needs can’t be

solved by a relational database easily. As the size and different types of data being generated and collected

continues to rise, it becomes increasingly obvious that we are moving beyond the capabilities of even the

best-of-class enterprise relational database systems.

Another thing to consider is how big data changes how we keep (i.e. store) data. On-premises storage is

expensive, but with cloud storage being so inexpensive, companies can hang on to their data much longer.

As cheap as cloud storage is, companies can keep every raw data point, from clickstream, weblogs, and

telemetry data. Many companies today are storing tens of petabytes of data in the cloud.

Which leads us to the need to understand big data and ultimately the Internet of Things (IoT). In this

chapter, I will begin by looking at the overall topic of big data, its characteristics, and how we should think

about it. I will use that foundation to discuss the hot topic of the Internet of Things. I’ll then wrap up the

chapter by looking at several scenarios where both big data and IoT are a common place to provide a visual

and foundation for the rest of this book.

Chapter 1 ■ the World of Big data and iot

From there, as architectures grew and changed, the need for more advanced data management systems

became necessary along with the need to do some level of advanced analytics. For example, a popular

scenario was the need to start studying customers and what they were buying. The analytics allowed, for

example, businesses to provide purchase recommendations and other opportunities to drive business

opportunities.

This scenario, along with more detailed data insight requirements, also brought around more powerful

database that added more analytics processing, dealing with cubes, dimensions, and fact tables for doing

OLAP analysis. Modern databases provided greater power and additional proficiencies for accessing

information through query syntaxes such as SQL. This also abstracted the complexity of the underlying

storage mechanism, making it easy to work with relational data and the volume of data it supported.

But there quickly came a point when we needed more than what current enterprise-level relational

database systems provided. Exponential data growth was a reality and we needed deeper and distinct

analysis on historical, current, and future data. This was critical to business success. The need to look and

analyze each individual record was right in front of us, but the infrastructure and current data management

systems didn’t and couldn’t scale. It was difficult to take current systems and infrastructure and apply them

to the deeper analytics of analyzing individual transactions.

This isn’t to say that the current architecture and systems are going away. There is and will be a need

for powerful relational database and business intelligence systems like SQL Server and SQL Server Analysis

Services. But as the amount data grew and the need to get deeper insights into that data was needed, it was

hard to take the same processing, architecture, and analysis model and scale it to meet the requirements of

storing and processing very granular transactions.

The strain on the data architecture along with the different types of data has challenged us to come up

with more efficient and scalable forms of storing and processing information. Our data wasn’t just relational

anymore. It was coming in the forms of social media, sensor and device information, weblogs, and more.

About this time we saw the term “No-SQL” bounced around. Many thought that this meant that relational

was dead. But in reality the term means “not only SQL,” meaning that data comes in all forms and types:

relational and non-, or semi-, relational.

Thus, in essence, we can think about big data as representing the vast amounts and different types of

data that organizations continue to work with, and try to gain insights into, on a daily basis. One attribute

of big data is that it can be generated at a very rapid rate, sometimes called a “fire hose” rate. The data is

valuable but previously not practical to store or analyze due to the cost of appropriate methods.

Additionally, when we talk about solutions to big data, it’s just not about the data itself; it also includes

the systems and processes to uncover and gain insights hidden inside all that wonderful, sweet data. Today’s

big data solutions are comprised of a set of technologies and processes that allow you to efficiently store and

analyze the data to gain the desired data insights.

The Three Vs of Big Data

Companies begin to look at big data solutions when their current traditional database systems reach the

limit of performance, scale, and cost. These big data solutions, in addition to helping overcome database

system limitations, also provide a way to more effectively provide the much-needed avenue for gaining

the greatly required insight into the data to further examine and mine data in a way that isn’t possible in

database systems. As such, big data is typically described and defined in terms of the three Vs to understand

big data and solve big data issues: volume, variety, and velocity.

Volume

The volume of data has reference to the scale of data. Where relational database systems work in the high

terabyte range, big data solutions store and process hundreds or thousands of terabytes, petabytes, and even

exabytes, with the total volume growing exponentially. A big data solution must have the storage to handle

and manage this volume of data and be designed to scale and work efficiently across multiple machines in a

distributed environment. Organizations today need to handle mass quantities of data each day.

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