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.
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