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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.
- Sales Rank: #1570538 in Books
- Published on: 2014-12-14
- Original language: English
- Number of items: 1
- Dimensions: 9.02" h x .17" w x 5.98" l, .30 pounds
- Binding: Paperback
- 60 pages
About the Author
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.
Most helpful customer reviews
5 of 5 people found the following review helpful.
Decent TSDB overview
By Erik Gfesser
This book specifically centers around time series databases (TSDB). Although collecting and analyzing time series data is not new, as the authors explain, the current task of building scalable time series databases is a huge challenge, calling for new approaches and new tools in light of the immense volume, velocity, and variety of data especially associated with machine data.
While time series databases are not a distinct category of databases apart from relational, key-value, column-oriented, document-oriented, and graph databases discussed in such books as "Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement" (see my review), use of databases for time series data serves a distinct purpose. For example, Apache HBase is a column-oriented database that can be used for time series data.
Dunning and Friedman first explain the value of using time series data, and present an overview of modern use cases followed by a comparison of relational databases versus non-relational databases in the context of time series data. The latter half of the text provides an explanation of the concepts involved in building a high performance time series database, followed by some brief discussion on related topics.
The authors correctly state in their introductory chapter that time series data indicates when something actually took place. Because data can be recorded long after it is measured, time series data does not actually indicate when it was recorded. Such data requires a bitemporal database, which is beyond the scope of this book. Readers might be interested in knowing that an excellent (although very long-winded) book exists on this subject entitled "Managing Time in Relational Databases: How to Design, Update, and Query Temporal Data" (see my review).
Readers should be aware that Hadoop-based databases Apache HBase and MapR-DB are the focus in this book because they can rapidly ingest time series data, and also provide support for rapid, efficient queries of time series data. Usage of Open TSDB and Grafana alongside either of these databases is also discussed, just realize that MapR-DB extensions that enable direct BLOB loading are presented as the solution for accelerated performance.
Of the 8 chapters in this book, Chapter 3 ("Storing and Processing Time Series Data") and Chapter 4 ("Practical Time Series Tools") are the core of the book. Conversely, Chapters 5 through 8, while providing brief glimpses into some interesting related topics, do not provide much value and can be safely skipped by most readers. A decent 70-page read available free from O'Reilly that can be consumed in an afternoon.
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