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.
Table of Contents
- 1. Time Series Data: Why Collect It?
- 2. A New World for Time Series Databases
- 3. Storing and Processing Time Series Data
4. Practical Time Series Tools
- Introduction to Open TSDB: Benefits and Limitations
- Architecture of Open TSDB
- Value Added: Direct Blob Loading for High Performance
- A New Twist: Rapid Loading of Historical Data
- Summary of Open Source Extensions to Open TSDB for Direct Blob Loading
- Accessing Data with Open TSDB
- Working on a Higher Level
- Accessing Open TSDB Data Using SQL-on-Hadoop Tools
- Using Apache Spark SQL
- Why Not Apache Hive?
- Adding Grafana or Metrilyx for Nicer Dashboards
- Possible Future Extensions to Open TSDB
- 5. Solving a Problem You Didn’t Know You Had
- 6. Time Series Data in Practical Machine Learning
- 7. Advanced Topics for Time Series Databases
- 8. What’s Next?
- A. Resources
- About the Authors
- Title: Time Series Databases: New Ways to Store and Access Data
- Release date: December 2014
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491914724