Book Description
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.
What You Will Learn
- Overcome the challenges IoT data brings to analytics
- Understand the variety of transmission protocols for IoT along with their strengths and weaknesses
- Learn how data flows from the IoT device to the final data set
- Develop techniques to wring value from IoT data
- Apply geospatial analytics to IoT data
- Use machine learning as a predictive method on IoT data
- Implement best strategies to get the most from IoT analytics
- Master the economics of IoT analytics in order to optimize business value
Table of Contents
1: Defining IoT Analytics and Challenges
- The situation
- Defining IoT analytics
- IoT analytics challenges
- Business value concerns
- Summary
2: IoT Devices and Networking Protocols
- IoT devices
- Networking basics
- IoT networking connectivity protocols
- IoT networking data messaging protocols
- Analyzing data to infer protocol and device characteristics
- Summary
3: IoT Analytics for the Cloud
- Building elastic analytics
- Elastic analytics concepts
- Designing for scale
- Cloud security and analytics
- The AWS overview
- Microsoft Azure overview
- The ThingWorx overview
- Summary
4: Creating an AWS Cloud Analytics Environment
- The AWS CloudFormation overview
- The AWS Virtual Private Cloud (VPC) setup walk-through
- How to terminate and clean up the environment
- Summary
5: Collecting All That Data - Strategies and Techniques
- Designing data processing for analytics
- Applying big data technology to storage
- Apache Spark for data processing
- To stream or not to stream
- Handling change
- Summary
6: Getting to Know Your Data - Exploring IoT Data
- Exploring and visualizing data
- Look for attributes that might have predictive value
- R (the pirate's language...if he was a statistician)
- Summing it all up
- Solving industry-specific analysis problems
- Summary
7: Decorating Your Data - Adding External Datasets to Innovate
- Adding internal datasets
- Adding external datasets
- Summary
8: Communicating with Others - Visualization and Dashboarding
- Common mistakes when designing visuals
- The Hierarchy of Questions method
- Designing visual analysis for IoT data
- Creating a dashboard with Tableau
- Creating and visualizing alerts
- Summary
9: Applying Geospatial Analytics to IoT Data
- Why do you need geospatial analytics for IoT?
- The basics of geospatial analysis
- Vector-based methods
- Raster-based methods
- Storing geospatial data
- Processing geospatial data
- Solving the pollution reporting problem
- Summary
10: Data Science for IoT Analytics
- Machine learning (ML)
- Anomaly detection using R
- Forecasting using ARIMA
- Deep learning
- Summary
11: Strategies to Organize Data for Analytics
- Linked Analytical Datasets
- Managing data lakes
- The data retention strategy
- Summary
12: The Economics of IoT Analytics
- The economics of cloud computing and open source
- Cost considerations for IoT analytics
- Thinking about revenue opportunities
- The economics of predictive maintenance example
- Summary
13: Bringing It All Together
- Review
- A sample project
- Summary
