Titlebook: Big Data SMACK; A Guide to Apache Sp Raul Estrada,Isaac Ruiz Book 2016 Raul Estrada and Isaac Ruiz 2016 Big Data.Scala.Akka.Apache Spark.Ap

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https://doi.org/10.1007/3-7643-7387-3We are reaching the end of this trip. In this chapter, you will learn how to create your own cluster in a simple way.

創(chuàng)造性

Genes in Populations: Forward in TimeThe goal of this chapter is to get you familiar with Apache Kafka and show you how to solve the consumption of millions of messages in a pipeline architecture. Here we show some Scala examples to give you a solid foundation for the different types of implementations and integrations for Kafka producers and consumers.

商議

Genes in Populations: Forward in TimeIn this chapter, we examine well-known patterns in developing fast data applications. As you know, there are two approaches: (1) the batch, on disk, traditional approach and (2) the streaming, on memory, modern approach. The patterns in this chapter apply to both approaches.

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Introduction to Computational CardiologyWell, we have reached the chapter where we have to connect everything, especially theory and practice. This chapter has two parts: the first part is an enumeration of the data pipeline strategies and the second part is how to connect the technologies.

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Big Data, Big SolutionsMany systems are monitoring a continuous stream of events: weather events, GPS signals, vital signs, logs, device metrics…. The list is endless. The natural way to collect and analyze this information is as a stream of data.

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The Language: ScalaThe main part of the SMACK stack is Spark, but sometimes the S is for Scala. You can develop in Spark in four languages: Java, Scala, Python, and R. Because Apache Spark is written in Scala, and this book is focused on streaming architecture, we are going to show examples in only the Scala language.

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