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Pro Spark streaming : the zen of real-time analytics using Apache Spark /
Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach,...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[New York] :
Apress,
[2016]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Nabi, Zubair, |e author. | |
| 245 | 1 | 0 | |a Pro Spark streaming : |b the zen of real-time analytics using Apache Spark / |c Zubair Nabi. |
| 264 | 1 | |a [New York] : |b Apress, |c [2016] | |
| 264 | 2 | |a New York, NY : |b Distributed to the Book trade worldwide by Springer Science+Business Media, |c [2016] | |
| 264 | 4 | |c ©2016 | |
| 300 | |a 1 online resource (1 volume) : |b illustrations | ||
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| 588 | 0 | |a Online resource; title from cover (Safari, viewed July 13, 2016). | |
| 500 | |a Place of publication from publisher's Web site. | ||
| 505 | 0 | |a Chapter 1: The Hitchhiker's Guide to Big Data -- Chapter 2: Introduction to Spark -- Chapter 3: DStreams: Realtime RDDs -- Chapter 4: High Velocity Streams: Parallelism and Other Stories -- Chapter 5: Real-time Route 66: Linking External Data Sources -- Chapter 6: The Art of Side Effects -- Chapter 7: Getting Ready for Prime Time -- Chapter 8: Real-time ETL and Analytics Magic -- Chapter 9: Machine Learning at Scale -- Chapter 10: Of Clouds, Lambdas, and Pythons. | |
| 520 | |a Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT. In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming. What You'll Learn Discover Spark Streaming application development and best practices Work with the low-level details of discretized streams Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integrate and couple with HBase, Cassandra, and Redis Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Use streaming machine learning, predictive analytics, and recommendations Mesh batch processing with stream processing via the Lambda architecture Who This Book Is For Data scientists, big data experts, BI analysts, and data architects. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 630 | 0 | 0 | |a Spark (Electronic resource : Apache Software Foundation) |
| 630 | 0 | 7 | |a Spark (Electronic resource : Apache Software Foundation) |2 fast |0 (OCoLC)fst01938143 |
| 650 | 0 | |a Streaming technology (Telecommunications) | |
| 650 | 0 | |a Big data. | |
| 650 | 6 | |a En continu (Télécommunications) | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Streaming technology (Telecommunications) |2 fast |0 (OCoLC)fst01134637 | |
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