LEADER 04423nam 2200649 450 001 9910786693703321 005 20230315152855.0 010 $a1-118-83802-5 010 $a1-118-83793-2 035 $a(CKB)3710000000134470 035 $a(EBL)1719571 035 $a(OCoLC)881888045 035 $a(SSID)ssj0001471753 035 $a(PQKBManifestationID)11783250 035 $a(PQKBTitleCode)TC0001471753 035 $a(PQKBWorkID)11432200 035 $a(PQKB)10423026 035 $a(Au-PeEL)EBL1719571 035 $a(CaPaEBR)ebr11022791 035 $a(CaONFJC)MIL769951 035 $a(CaSebORM)9781118838020 035 $a(MiAaPQ)EBC1719571 035 $a(PPN)197697909 035 $a(EXLCZ)993710000000134470 100 $a20150305h20142014 uy 0 101 0 $aeng 135 $aurunu||||| 181 $ctxt 182 $cc 183 $acr 200 10$aReal-time analytics $etechniques to analyze and visualize streaming data /$fByron Ellis 205 $a1st edition 210 1$aIndianapolis, Indiana :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (841 p.) 300 $aIncludes index. 311 $a1-118-83791-6 327 $aCover; 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; Conclusion 327 $aChapter 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 Applications 327 $aVisualizing 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 Sketches 327 $aThe 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 Agreement 330 $aConstruct 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, 606 $aReal-time data processing 606 $aData flow computing 606 $aData mining 615 0$aReal-time data processing. 615 0$aData flow computing. 615 0$aData mining. 676 $a025.04 700 $aEllis$b Byron$0743000 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910786693703321 996 $aReal-time analytics$91477215 997 $aUNINA