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.
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.
Time Series Databases: New Ways to Store and Access Data
Ted Dunning is Chief Applications Architect at MapR Technologiesand active in the open source community, being committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and serves as a mentor for these Apache projects: Storm, Flink, Optiq, Datafu and Drill. He has contributed to Mahout clustering, classification, matrix decomposition algorithms and new Mahout Math library, and recently designed the t-digest algorithm used in several open source projects. He also architected the modifications for Open TSDB described in this book.
Ted was the chief architect behind the MusicMatch (now Yahoo Music)and Veoh recommendation systems, built fraud-detection systems forID Analytics (LifeLock), and has issued 24 patents to date. Ted has aPhD in computing science from University of Sheffield. When he’s notdoing data science, he plays guitar and mandolin. Ted is on Twitter at@ted_dunning.
Ellen Friedman is a solutions consultant and well known speaker and author, currently writing mainly about big data topics. She is a committer for the Apache Mahout project and a contributor to the Apache Drill project. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics including molecular biology, nontraditional inheritance, and oceanography. Ellen is also co-author of a book of magic-themed cartoons, A Rabbit Under the Hat. Ellen is on Twitter at @Ellen_Friedman.