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.
