Book Image

Analytics for the Internet of Things (IoT)

By : Andrew Minteer
5 (1)
Book Image

Analytics for the Internet of Things (IoT)

5 (1)
By: Andrew Minteer

Overview of this book

We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You’ll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We’ll also review the economics of IoT analytics and you’ll discover ways to optimize business value. By the end of the book, you’ll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

To stream or not to stream


Streams are datasets that continuously update as each new data message arrives with little to no latency. Streaming analytics operate on this continuously updating dataset at much shorter intervals than batch processing. Real-time analytics is a little bit of a misnomer when applied to streaming analytics as intervals are typically in minutes rather than continuously ongoing. The frequency affects processing and technology requirements, so intervals should be set for longer time periods if possible in order to save costs.

Stream datasets normally keep data for a window of time, and then discard it. There are specialized technology and processing options to handle streams, which are, for the most part, in addition to requirements for long term big data store technology we have focused on in this chapter. Amazon Kinesis is an example of a specialized data streaming technology service.

The technology and the programming code base needed to support analytics are (usually...