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  • Book Overview & Buying Mastering Apache Spark 2.x
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Mastering Apache Spark 2.x

Mastering Apache Spark 2.x - Second Edition

By : Romeo Kienzler
4.5 (2)
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Mastering Apache Spark 2.x

Mastering Apache Spark 2.x

4.5 (2)
By: Romeo Kienzler

Overview of this book

Apache Spark is an in-memory, cluster-based Big Data processing system that provides a wide range of functionalities such as graph processing, machine learning, stream processing, and more. This book will take your knowledge of Apache Spark to the next level by teaching you how to expand Spark’s functionality and build your data flows and machine/deep learning programs on top of the platform. The book starts with a quick overview of the Apache Spark ecosystem, and introduces you to the new features and capabilities in Apache Spark 2.x. You will then work with the different modules in Apache Spark such as interactive querying with Spark SQL, using DataFrames and DataSets effectively, streaming analytics with Spark Streaming, and performing machine learning and deep learning on Spark using MLlib and external tools such as H20 and Deeplearning4j. The book also contains chapters on efficient graph processing, memory management and using Apache Spark on the cloud. By the end of this book, you will have all the necessary information to master Apache Spark, and use it efficiently for Big Data processing and analytics.
Table of Contents (15 chapters)
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10
Deep Learning on Apache Spark with DeepLearning4j and H2O

Example--Apache Spark on Kubernetes

This example is taken directly from the Kubernetes GitHub page, which can be found at https://github.com/Kubernetes/Kubernetes/tree/master/examples/spark. We've done some modification to that example, since we are using a very specific Kubernetes deployment called Minikube. But we still want it to be based on the original example, since when you are using this link, you are guaranteed to always obtain an updated version compatible with the latest Kubernetes version in place. So these are the required steps, which are explained in detail in the next sections:

  1. Install Minikube local Kubernetes to your machine.
  2. Deploy the Apache Spark master node.
  3. Deploy the Apache Spark worker nodes.
  4. Deploy the Apache Kubernetes notebook application and test the whole cluster (optional).

The following section describes the prerequisites to run the example...

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Mastering Apache Spark 2.x
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