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Apache Spark 2.x for Java Developers

You're reading from   Apache Spark 2.x for Java Developers Explore big data at scale using Apache Spark 2.x Java APIs

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Length 350 pages
Edition 1st Edition
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Concepts
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Authors (2):
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Sourav Gulati Sourav Gulati
Author Profile Icon Sourav Gulati
Sourav Gulati
Sumit Kumar Sumit Kumar
Author Profile Icon Sumit Kumar
Sumit Kumar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Introduction to Spark FREE CHAPTER 2. Revisiting Java 3. Let Us Spark 4. Understanding the Spark Programming Model 5. Working with Data and Storage 6. Spark on Cluster 7. Spark Programming Model - Advanced 8. Working with Spark SQL 9. Near Real-Time Processing with Spark Streaming 10. Machine Learning Analytics with Spark MLlib 11. Learning Spark GraphX

Summary

This chapter focused on handling streaming data from sources such as Kafka, socket, and filesystem. We also covered various stateful and stateless transformation of DStream along with checkpointing of data. But chekpointing of data alone does not guarantee fault tolerance and hence we discussed other approaches to make Spark Streaming job fault tolerant. We also talked about the transform operation, which comes in handy where operations of RDD API is not available in DStreams. Spark 2.0 introduced structured streaming as a separate module, however, because of its similarity with Spark Streaming, we discussed the newly introduced APIs of structured streaming also.

In the next chapter, we will focus on introducing the concepts of machine learning and then move towards its implementation using Apache Spark MLlib libraries. We will also discuss some real-world problems using...

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