Real-time analytics techniques to analyze and visualize streaming data
Construct 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 effectiv...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Indianapolis, Indiana :
Wiley
2014.
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629542006719 |
Tabla de Contenidos:
- 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; Conclusion
- Chapter 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
- Visualizing 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
- The 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
