Cargando…

Practical real-time data processing and analytics: distributed computing and event processing using Apache Spark, Flink, Storm, and Kafka

Detalles Bibliográficos
Autores principales:	Saxena, Shilpi, Gupta, Saurabh
Lenguaje:	eng
Publicado:	Packt Publishing 2017
Materias:	Computing and Computers
Acceso en línea:	http://cds.cern.ch/record/2294081

Ejemplares similares

Apache Flink: Distributed Stream Data Processing
por: Jacobs, Kevin, et al.
Publicado: (2016)

Learning Apache Flink: discover the definitive guide to crafting lightning-fast data processing for distributed systems with Apache Flink
por: Deshpande, Tanmay
Publicado: (2017)

Introduction to Apache Flink: stream processing for real time and beyond
por: Friedman, B Ellen, et al.
Publicado: (2016)

Stream processing with Apache Flink: fundamentals, implementation, and operation of streaming applications
por: Hueske, Fabian, et al.
Publicado: (2019)

Apache Kafka
por: Garg, Nishant
Publicado: (2013)

Apache Kafka handbook: over 50 hands-on recipes to efficiently administer, maintain, and use your Apache Kafka installation
por: Minni, Saurabh
Publicado: (2015)

Learning Apache Kafka
por: Garg, Nishant
Publicado: (2015)

Pro Spark streaming: the zen of real-time analytics using Apache Spark
por: Nabi, Zubair
Publicado: (2016)

Mastering Apache Spark: gain expertise in processing and storing data by using advanced techniques with Apache Spark
por: Frampton, Mike, et al.
Publicado: (2015)

Apache Spark graph processing: build, process, and analyze large-scale graphs with Spark
por: Ramamonjison, Rindra, et al.
Publicado: (2015)

Stream processing with Apache Spark: mastering structured streaming and Spark streaming
por: Maas, Gerard, et al.
Publicado: (2019)

PySpark cookbook: over 60 recipes for implementing big data processing and analytics using Apache Spark and Python
por: Lee, Denny, et al.
Publicado: (2018)

Mastering Apache Storm: processing big data streams in real time
por: Jain, Ankit
Publicado: (2017)

Making sense of stream processing: the philosophy behind Apache Kafka and scalable stream data platforms
por: Kleppmann, Martin
Publicado: (2016)

Apache Spark 2.x cookbook: Cloud-ready recipes to do analytics and data science on Apache Spark
por: Yadav, Rishi
Publicado: (2017)

Apache Spark for data science cookbook: overinsightful 90 recipes to get lightning-fast analytics with Apache Spark
por: Chitturi, Padma Priya
Publicado: (2016)

Hands-on deep learning with Apache Spark: build and deploy distributed deep learning applications on Apache Spark
por: Iozzia, Guglielmo
Publicado: (2019)

Scala and Spark for big data analytics: tame big data with Scala and Apache Spark!
por: Karim, Md Rezaul
Publicado: (2017)

Apache Spark 2 for beginners: develop large-scale distributed data processing applications using Spark 2 in Scala and Python
por: Thottuvaikkatumana, Rajanarayanan
Publicado: (2016)

Learning Apache Spark 2: process big data with the speed of light!
por: Abbasi, Muhammad Asif
Publicado: (2017)

Apache Spark deep learning cookbook: over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
por: Sherif, Ahmed, et al.
Publicado: (2018)

Apache Kafka 1.0 cookbook: over 100 practical recipes on using distributed enterprise messaging to handle real-time data
por: Estrada, Raul
Publicado: (2017)

Big data processing with Apache Spark: efficiently tackle large datasets and big data analysis with Spark and Python
por: Franco Galeano, Manuel Ignacio
Publicado: (2018)

Designing even-driven systems: concepts and patterns for streaming services with Apache Kafka
por: Roberts, Mike
Publicado: (2018)

Streaming architecture: new designs using Apache Kafka and MapR streams
por: Dunning, Ted, et al.
Publicado: (2016)

Kafka: the definitive guide : real-time data and stream processing at scale
por: Narkhede, Neha, et al.
Publicado: (2017)

Frank Kane's Taming big data with Apache Spark and Python: real-world examples to help you analyze large datasets with Apache Spark
por: Kane, Frank
Publicado: (2017)

Mastering Apache Spark 2.x: scalable analytics faster than ever
por: Kienzler, Romeo
Publicado: (2017)

Real-time big data analytics: design, process, and analyze large sets of complex data in real time
por: Gupta, Sumit, et al.
Publicado: (2016)

Apache Kafka quick start guide: leverage Apache Kafka 2.0 to simplify real-time data processing for distributed applications
por: Estrada, Raúl
Publicado: (2018)

Beginning Apache Spark 2: with resilient distributed datasets, Spark SQL, structured streaming and Spark machine learning library
por: Luu, Hien
Publicado: (2018)

Building data streaming applications with Apache Kafka: designing and deploying enterprise messaging queues
por: Kumar, Manish, et al.
Publicado: (2017)

Scala programming for big data analytics: get started with big data analytics using Apache Spark
por: Elahi, Irfan
Publicado: (2019)

Storm applied: strategies for real-time event processing
por: Allen, Sean T, et al.
Publicado: (2015)

Apache Spark quick start guide: quickly learn the art of writing efficient big data applications with Apache Spark
por: Mehrotra, Shrey, et al.
Publicado: (2019)

High performance Spark: best practices for scaling and optimizing Apache Spark
por: Karau, Holden, et al.
Publicado: (2017)

Kafka Streams in action: real-time apps and microservices with the Kafka Streams API
por: Bejeck, William P
Publicado: (2018)

Practical Apache Spark: using the Scala API
por: Chellappan, Subhashini, et al.
Publicado: (2018)

Apache Spark 2.x for Java developers: explore data at scale using the Java APIs of Apache Spark 2.x
por: Gulati, Sourav, et al.
Publicado: (2017)

Big data analytics beyond Hadoop : real-time applications with Storm, Spark, and more Hadoop alternatives
por: Agneeswaram, Vijay Srinivas
Publicado: (2014)

Cannot write session to /tmp/vufind_sessions/sess_qided6rsk1ealcansp41iafgq3