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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.
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Über die Autorin bzw. den Autor
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.
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Time Series Databases
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Zustand: Como nuevo. : Este libro conciso te muestra formas efectivas de recopilar, persistir y acceder a datos de series temporales a gran escala para su análisis. Explorarás la teoría detrás de las bases de datos de series temporales y aprenderás métodos prácticos para implementarlas. Los autores Ted Dunning y Ellen Friedman brindan un examen detallado de herramientas de código abierto como OpenTSDB y nuevas modificaciones que aceleran enormemente la ingesta de datos. Aprenderás una variedad de casos de uso de series temporales, las ventajas de las bases de datos NoSQL para datos de series temporales a gran escala, el diseño de tablas NoSQL para bases de datos de series temporales de alto rendimiento, los beneficios y las limitaciones de OpenTSDB, cómo acceder a los datos en OpenTSDB usando R, Go y Ruby, cómo las bases de datos de series temporales contribuyen a proyectos prácticos de aprendizaje automático y cómo manejar la complejidad añadida de los datos geo-temporales. EAN: 9781491914724 Tipo: Libros Categoría: Tecnología|Ciencias Título: Time Series Databases Autor: Ted Dunning| Ellen Friedman Editorial: O'Reilly Media Idioma: en Páginas: 82 Formato: tapa blanda. Artikel-Nr. Happ-2024-01-19-d64adf05
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Time Series Databases New Ways to Store and Acces Data
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Time Series Databases: New Ways to Store and Access Data
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Time Series Databases: New Ways to Store and Access Data
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