Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis. You'll explore the theory behind time series databases and learn practical methods for implementing them. Authors Ted Dunning and Ellen Friedman provide a detailed examination of open source tools such as OpenTSDB and new modifications that greatly speed up data ingestion.

You'll learn:

• A variety of time series use cases
• The advantages of NoSQL databases for large-scale time series data
• NoSQL table design for high-performance time series databases
• The benefits and limitations of OpenTSDB
• How to access data in OpenTSDB using R, Go, and Ruby
• How time series databases contribute to practical machine learning projects
• How to handle the added complexity of geo-temporal data

For advice on analyzing time series data, check out Practical Machine Learning: A New Look at Anomaly Detection, also from Ted Dunning and Ellen Friedman.