Andrew Minteer’s Analytics for the Internet of Things (IoT) is a comprehensive guidebook for anyone looking to extract actionable intelligence from the overwhelming flood of data generated by IoT devices. With a 4.6 out of 5 rating and 11 reviews, this book provides insight into how IoT data can be analyzed to drive better business decisions and greater control of IoT infrastructure.
The book starts by highlighting the challenge of extracting value from vast amounts of barely intelligible data generated by IoT devices. It then discusses how IoT devices generate data, how the information travels over networks, and how it is collected, stored, and analyzed. The author explains strategies to optimize data storage, handle data quality concerns, and manage all that data, highlighting techniques to wring value from IoT data.
Cloud resources are an excellent match for IoT analytics, so the book provides detailed reviews of Amazon Web Services, Microsoft Azure, and PTC ThingWorx. Geospatial analytics is introduced as a way to leverage location information, and the book explains how environmental data can be combined with IoT data to enhance predictive capability. The economics of IoT analytics are also reviewed, with suggestions on how to optimize business value.
Analytics for the Internet of Things covers a broad range of topics, including machine learning, predictive modeling, and the economics of IoT analytics. It provides practical insights into handling scale for data storage and analytics and how Apache Spark can be leveraged to handle scalability. The book also explores using R and Python for analytic modeling and strategies to organize data for analytics.
The book is structured in an easy-to-follow manner, with each chapter building on the previous one. It begins by defining IoT analytics and discussing the challenges that come with it before delving into IoT devices and networking protocols. The book then explores IoT analytics for the cloud, providing detailed instructions on creating an AWS Cloud Analytics Environment.
Readers will learn to explore IoT data, add external datasets to innovate, visualize and dashboard IoT data, and apply geospatial analytics. The book also guides organizing data for analytics, the economics of IoT analytics, and how to bring everything together.
The author’s writing style is clear, concise, and easy to understand. The book provides numerous examples and use cases that illustrate the concepts presented. The author does an excellent job of breaking down complex concepts into easily digestible chunks, making this book accessible to technical and non-technical readers.
Overall, Analytics for the Internet of Things is an excellent resource for extracting value from IoT data. The book provides practical insights into analyzing IoT data and strategies to optimize business value. With clear writing, numerous examples, and practical insights, this book is a must-read for anyone working with IoT data.