Time Series Databases: New Ways to Store and Access Data

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.

Table of Contents

Chapter 1. Time Series Data: Why Collect It?
Chapter 2. A New World for Time Series Databases
Chapter 3. Storing and Processing Time Series Data
Chapter 4. Practical Time Series Tools
Chapter 5. Solving a Problem You Didn’t Know You Had
Chapter 6. Time Series Data in Practical Machine Learning
Chapter 7. Advanced Topics for Time Series Databases
Chapter 8. What’s Next?
Appendix A. Resources