Book Image

Designing Production-Grade and Large-Scale IoT Solutions

By : Mohamed Abdelaziz
Book Image

Designing Production-Grade and Large-Scale IoT Solutions

By: Mohamed Abdelaziz

Overview of this book

With the rising demand for and recent enhancements in IoT, a developer with sound knowledge of IoT is the need of the hour. This book will help you design, build, and operate large-scale E2E IoT solutions to transform your business and products, increase revenue, and reduce operational costs. Starting with an overview of how IoT technologies can help you solve your business problems, this book will be a useful guide to helping you implement end-to-end IoT solution architecture. You'll learn to select IoT devices; real-time operating systems; IoT Edge covering Edge location, software, and hardware; and the best IoT connectivity for your IoT solution. As you progress, you'll work with IoT device management, IoT data analytics, IoT platforms, and put these components to work as part of your IoT solution. You'll also be able to build IoT backend cloud from scratch by leveraging the modern app architecture paradigms and cloud-native technologies such as containers and microservices. Finally, you'll discover best practices for different operational excellence pillars, including high availability, resiliency, reliability, security, cost optimization, and high performance, which should be applied for large-scale production-grade IoT solutions. By the end of this IoT book, you'll be confident in designing, building, and operating IoT solutions.
Table of Contents (15 chapters)
Section 1: Anatomy of IoT
Section 2: The IoT Backend (aka the IoT Cloud)
Section 3: IoT Application Architecture Paradigms and IoT Operational Excellence

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...