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Sinopsis
Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You'll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs.
Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API.
- Learn fundamental stream processing concepts and examine different streaming architectures
- Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail
- Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs
- Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms
- Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams
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Acerca del autor
Francois Garillot worked on Scala's type system in 2006, earned his PhD from the French Ecole Polytechnique in 2011, and worked at Typesafe, after a brief stint in Internet advertising. He's worked on interactive interfaces to the Scala compiler, while nourishing a strong enthusiasm for data analytics in his spare time, until Apache Spark let him fullfill this passion as his main job. He received the first Spark Certification in November 2014, and worked in London and Philadelphia, among other places. In his spare time, he can be found practicing one of a half-dozen ways of making coffee, climbing up or skiing down a not-necessarily-Alpine mountain, or sailing a not-necessarily coastal course.
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Stream Processing with Apache Spark
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
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Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming (Paperback or Softback)
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O'Reilly Media 6/22/2019, 2019
ISBN 10: 1491944242
ISBN 13: 9781491944240
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Paperback or Softback. Condición: New. Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming 1.57. Book. Nº de ref. del artículo: BBS-9781491944240
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Stream Processing with Apache Spark
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Stream Processing with Apache Spark
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You'll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs. Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API. Learn fundamental stream processing concepts and examine different streaming architectures Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams. Nº de ref. del artículo: LU-9781491944240
Cantidad disponible: Más de 20 disponibles
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Stream Processing With Apache Spark : Mastering Structured Streaming and Spark Streaming
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Condición: New. Nº de ref. del artículo: 26314136-n
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Stream Processing with Apache Spark
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You'll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs. Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API. Learn fundamental stream processing concepts and examine different streaming architectures Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams. Nº de ref. del artículo: LU-9781491944240
Cantidad disponible: Más de 20 disponibles
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Stream Processing with Apache Spark
Publicado por
O'Reilly Media, Inc, USA, 2019
ISBN 10: 1491944242
ISBN 13: 9781491944240
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Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming
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Paperback. Condición: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks. Nº de ref. del artículo: Scanned1491944242
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Stream Processing With Apache Spark : Mastering Structured Streaming and Spark Streaming
Publicado por
O'Reilly Media, 2019
ISBN 10: 1491944242
ISBN 13: 9781491944240
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Stream Processing With Apache Spark : Mastering Structured Streaming and Spark Streaming
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