04423nam 2200649 450 991081408190332120230315152855.01-118-83802-51-118-83793-2(CKB)3710000000134470(EBL)1719571(OCoLC)881888045(SSID)ssj0001471753(PQKBManifestationID)11783250(PQKBTitleCode)TC0001471753(PQKBWorkID)11432200(PQKB)10423026(Au-PeEL)EBL1719571(CaPaEBR)ebr11022791(CaONFJC)MIL769951(CaSebORM)9781118838020(MiAaPQ)EBC1719571(PPN)197697909(EXLCZ)99371000000013447020150305h20142014 uy 0engurunu|||||txtccrReal-time analytics techniques to analyze and visualize streaming data /Byron Ellis1st editionIndianapolis, Indiana :Wiley,2014.©20141 online resource (841 p.)Includes index.1-118-83791-6 Cover; Chapter 1: Introduction to Streaming Data; Sources of Streaming Data; Why Streaming Data Is Different; Infrastructures and Algorithms; Conclusion; Part I: Streaming A Analytics Architecture; Chapter 2: Designing Real-Time Streaming Architectures; Real-Time Architecture Components; Features of a Real-Time Architecture; Languages for Real-Time Programming; A Real-Time Architecture Checklist; Conclusion; Chapter 3: Service Configuration and Coordination; Motivation for Configuration and Coordination Systems; Maintaining Distributed State; Apache ZooKeeper; ConclusionChapter 4: Data-Flow Management in Streaming Analysis Distributed Data Flows; Apache Kafka: High-Throughput Distributed Messaging; Apache Flume: Distributed Log Collection; Conclusion; Chapter 5: Processing Streaming Data; Distributed Streaming Data Processing; Processing Data with Storm; Processing Data with Samza; Conclusion; Chapter 6: Storing Streaming Data; Consistent Hashing; "NoSQL" Storage Systems; Other Storage Technologies; Choosing a Technology; Warehousing; Conclusion; Part II: Analysis and Visualization; Chapter 7: Delivering Streaming Metrics; Streaming Web ApplicationsVisualizing Data Mobile Streaming Applications; Conclusion; Chapter 8: Exact Aggregation and Delivery; Timed Counting and Summation; Multi-Resolution Time-Series Aggregation; Stochastic Optimization; Delivering Time-Series Data; Conclusion; Chapter 9: Statistical Approximation of Streaming Data; Numerical Libraries; Probabilities and Distributions; Working with Distributions; Random Number Generation; Sampling Procedures; Conclusion; Chapter 10: Approximating Streaming Data with Sketching; Registers and Hash Functions; Working with Sets; The Bloom Filter; Distinct Value SketchesThe Count-Min Sketch Other Applications; Conclusion; Chapter 11: Beyond Aggregation; Models for Real-Time Data; Forecasting with Models; Monitoring; Real-Time Optimization; Conclusion; Introduction; Overview and Organization of This Book; Who Should Read This Book; Tools You Will Need; What''s on the Website; Time to Dive In; End User License AgreementConstruct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development,Real-time data processingData flow computingData miningReal-time data processing.Data flow computing.Data mining.025.04Ellis Byron743000MiAaPQMiAaPQMiAaPQBOOK9910814081903321Real-time analytics1477215UNINA