國立虎尾科技大學 |

Pro Spark streaming = the zen of real-time analytics using Apache Spark /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Pro Spark streaming/ by Zubair Nabi.
其他題名:	the zen of real-time analytics using Apache Spark /
作者:	Nabi, Zubair.
出版者:	Berkeley, CA :Apress : : 2016.,
面頁冊數:	xix, 230 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Streaming technology (Telecommunications) -
電子資源:	http://dx.doi.org/10.1007/978-1-4842-1479-4
ISBN:	9781484214794

Pro Spark streaming = the zen of real-time analytics using Apache Spark /
Nabi, Zubair.

Pro Spark streamingthe zen of real-time analytics using Apache Spark /[electronic resource] :by Zubair Nabi. - Berkeley, CA :Apress :2016. - xix, 230 p. :ill., digital ;24 cm.

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.

Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. Pro Spark Streaming 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 the book 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: Spark Streaming application development and best practices Low-level details of discretized streams The application and vitality of streaming analytics to a number of industries and domains Optimization of production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingestion of data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integration and coupling with HBase, Cassandra, and Redis Design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Streaming machine learning, predictive analytics, and recommendations Meshing batch processing with stream processing via the Lambda architecture Who This Book Is For: The audience includes data scientists, big data experts, BI analysts, and data architects.

ISBN: 9781484214794

Standard No.: 10.1007/978-1-4842-1479-4doiSubjects--Uniform Titles:

Spark (Electronic resource : Apache Software Foundation)
Subjects--Topical Terms:

657691
Streaming technology (Telecommunications)

LC Class. No.: TK5105.386

Dewey Class. No.: 006.7876

Pro Spark streaming = the zen of real-time analytics using Apache Spark /
LDR:03629nam a2200337 a 4500 001 865396
003 DE-He213
005 20161202141047.0
006 m d
007 cr nn 008maaau
008 170720s2016 cau s 0 eng d
020 $a 9781484214794 $q (electronic bk.)
020 $a 9781484214800 $q (paper)
024 7 $a 10.1007/978-1-4842-1479-4 $2 doi
035 $a 978-1-4842-1479-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5105.386
072 7 $a JPP $2 bicssc
072 7 $a UB $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a POL017000 $2 bisacsh
082 0 4 $a 006.7876 $2 23
090 $a TK5105.386 $b .N116 2016
100 1 $a Nabi, Zubair. $3 1110909
245 1 0 $a Pro Spark streaming $h [electronic resource] : $b the zen of real-time analytics using Apache Spark / $c by Zubair Nabi.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2016.
300 $a xix, 230 p. : $b ill., digital ; $c 24 cm.
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. Pro Spark Streaming 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 the book 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: Spark Streaming application development and best practices Low-level details of discretized streams The application and vitality of streaming analytics to a number of industries and domains Optimization of production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingestion of data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integration and coupling with HBase, Cassandra, and Redis Design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Streaming machine learning, predictive analytics, and recommendations Meshing batch processing with stream processing via the Lambda architecture Who This Book Is For: The audience includes data scientists, big data experts, BI analysts, and data architects.
630 0 0 $a Spark (Electronic resource : Apache Software Foundation) $3 1110910
650 0 $a Streaming technology (Telecommunications) $3 657691
650 0 $a Big data. $3 981821
650 1 4 $a Computer Science. $3 593922
650 2 4 $a Computer Appl. in Administrative Data Processing. $3 669633
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-1-4842-1479-4
950 $a Professional and Applied Computing (Springer-12059)