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Designing Production-Grade and Large-Scale IoT Solutions

You're reading from   Designing Production-Grade and Large-Scale IoT Solutions A comprehensive and practical guide to implementing end-to-end IoT solutions

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781838829254
Length 412 pages
Edition 1st Edition
Languages
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Author (1):
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Mohamed Abdelaziz Alwan Mohamed Abdelaziz Alwan
Author Profile Icon Mohamed Abdelaziz Alwan
Mohamed Abdelaziz Alwan
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Anatomy of IoT
2. Chapter 1: Introduction to the IoT – The Big Picture FREE CHAPTER 3. Chapter 2: The "I" in IoT – IoT Connectivity 4. Chapter 3: The "T" in IoT – Devices and Edge 5. Section 2: The IoT Backend (aka the IoT Cloud)
6. Chapter 4: Diving Deep into the IoT Backend (the IoT Cloud) 7. Chapter 5: Exploring IoT Platforms 8. Chapter 6: Understanding IoT Device Management 9. Chapter 7: In the End, It Is All about Data, Isn't it? 10. Section 3: IoT Application Architecture Paradigms and IoT Operational Excellence
11. Chapter 8: IoT Application Architecture Paradigms 12. Chapter 9: Operational Excellence Pillars for Production-Grade IoT Solutions 13. Chapter 10: Wrapping Up and Final Thoughts 14. Other Books You May Enjoy

An IoT data analytics overview

Traditional data analytics or Business Intelligence (BI) solutions have been available for decades now. They follow the standard and well-known data analytics or data mining process that starts with data extraction from source systems. This is followed by the data transformation process and then loading data into purpose-fit data analytics stores, such as SQL-based data warehouses or a relational database. This process is usually called Extract, Transform, and Load (ETL).

Traditional data analytics is different from modern or advanced data analytics; in traditional data analytics, a business analyst or business owner starts by already having data from different data sources. Then, they will ask the question, OK I have all the data – what kind of information will I get out of such raw data? Then, they ask the question, Now that I have the information from the raw data, what kind of business insights will I get out of such valuable information...

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