A Comparison Of Nosql Time Series Databases
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A Comparison of NoSQL Time Series Databases
Research Paper (undergraduate) from the year 2015 in the subject Engineering - Industrial Engineering and Management, grade: 1,0, Technical University of Berlin (Wirtschaftsinformatik - Information Systems Engineering (ISE)), course: Seminar: Hot Topics in Information Systems Engineering, language: English, abstract: During the last years NoSQL databases have been developed to ad-dress the needs of tremendous performance, reliability and horizontal scalability. NoSQL time series databases (TSDBs) have risen to combine valuable NoSQL properties with characteristics of time series data encountering many use-cases. Solutions offer the efficient handling of data volume and frequency related to time series. Developers and decision makers struggle with the choice of a TSDB among a large variety of solutions. Up to now no comparison exists focusing on the specific features and qualities of those heterogeneous applications. This paper aims to deliver two frameworks for the comparison of TSDBs, firstly with a focus on features and secondly on quality. Furthermore, we apply and evaluate the frameworks on up to seven open-source TSDBs such as InfluxDB and OpenTSDB. We come to the result that the investigated TSDBs differ mainly in support- and extension related points. They share performance-enhancing techniques, time-related query capabilities and data schemas optimized for the handling of time-series data.
Time Series Databases
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.
Graph Databases
With social media producing such huge amounts of data, the importance of gathering this rich data, often called "the digital gold rush", processing it and retrieving information is vital. This practical book combines various state-of-the-art tools, technologies and techniques to help us understand Social Media Analytics, Data Mining and Graph Databases, and how to better utilize their potential. Graph Databases: Applications on Social Media Analytics and Smart Cities reviews social media analytics with examples using real-world data. It describes data mining tools for optimal information retrieval; how to crawl and mine data from Twitter; and the advantages of Graph Databases. The book is meant for students, academicians, developers and simple general users involved with Data Science and Graph Databases to understand the notions, concepts, techniques, and tools necessary to extract data from social media, which will aid in better information retrieval, management and prediction.