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Apache Spark 2: Data Processing and Real-Time Analytics

You're reading from   Apache Spark 2: Data Processing and Real-Time Analytics Master complex big data processing, stream analytics, and machine learning with Apache Spark

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789959208
Length 616 pages
Edition 1st Edition
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Authors (7):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
Romeo Kienzler Romeo Kienzler
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Romeo Kienzler
Siamak Amirghodsi Siamak Amirghodsi
Author Profile Icon Siamak Amirghodsi
Siamak Amirghodsi
Broderick Hall Broderick Hall
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Broderick Hall
Md. Rezaul Karim Md. Rezaul Karim
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Md. Rezaul Karim
Meenakshi Rajendran Meenakshi Rajendran
Author Profile Icon Meenakshi Rajendran
Meenakshi Rajendran
Shuen Mei Shuen Mei
Author Profile Icon Shuen Mei
Shuen Mei
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Toc

Table of Contents (23) Chapters Close

Title Page
Copyright
About Packt
Contributors
Preface
1. A First Taste and What's New in Apache Spark V2 FREE CHAPTER 2. Apache Spark Streaming 3. Structured Streaming 4. Apache Spark MLlib 5. Apache SparkML 6. Apache SystemML 7. Apache Spark GraphX 8. Spark Tuning 9. Testing and Debugging Spark 10. Practical Machine Learning with Spark Using Scala 11. Spark's Three Data Musketeers for Machine Learning - Perfect Together 12. Common Recipes for Implementing a Robust Machine Learning System 13. Recommendation Engine that Scales with Spark 14. Unsupervised Clustering with Apache Spark 2.0 15. Implementing Text Analytics with Spark 2.0 ML Library 16. Spark Streaming and Machine Learning Library 1. Other Books You May Enjoy Index

Streaming sources


We will not be able to cover all the stream types with practical examples in this section, but where this chapter is too small to include code, we will at least provide a description. In this chapter, we will cover the TCP and file streams and the Flume, Kafka, and Twitter streams. Apache Spark tends only to support this limited set out of the box, but this is not a problem since 3rd party developers provide connectors to other sources as well. We will start with a practical TCP-based example. This chapter examines stream processing architecture.

Note

For instance, what happens in cases where the stream data delivery rate exceeds the potential data processing rate? Systems such as Kafka provide the possibility of solving this issue by caching data until it is requested with the additional ability to use multiple data topics and consumers (publish-subscribe model).

TCP stream

There is a possibility of using the Spark Streaming Context method called socketTextStream to stream...

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