Читать книгу Computational Statistics in Data Science - Группа авторов - Страница 103

The demand for stream processing is on the increase, and data have to be processed fast to make decisions in real‐time. Because of the developing interest in streaming data analysis, a huge number of enormous streaming data solutions have been created both by the open‐source community and enterprise technology vendors [10]. As indicated by Millman [40], there are a few elements to consider while choosing data stream tools and technologies in request to settle on viable data management decisions. Those elements include the shape of the data, data accessibility, availability, and consistency requirement, and workload. Some prominent open‐source tools and technologies for data stream analytics include NoSQL [41], Apache Spark [42–44], Apache Storm [45], Apache Samza [46, 47], Yahoo! S4 [48], Photon [49], Apache Aurora [50], EsperTech [51], SAMOA [52], C‐SPARQL [53], CQELS [54], ETALIS [55], SpagoWorld [56]. Some proprietary tools and technologies for streaming data are Cloudet [57], Sentiment Brand Monitoring [58], Elastic Streaming Processing Engine [59], IBM InfoSphere Streams [16, 60, 61], Google MillWheel [46], Infochimps Cloud [56], Azure Stream [62], Microsoft Stream Insight [63], TIBCO StreamBase [64], Lambda Architecture [6], IoTSim‐Stream [65], and Apama Stream [62].