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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
Author Profile Icon Md. Rezaul Karim
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
+3 more Show less
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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 2. Apache Spark Streaming FREE CHAPTER 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

Chapter 13. Recommendation Engine that Scales with Spark

In this chapter, we will cover:

  • Setting up the required data for a scalable recommendation engine in Spark 2.0
  • Exploring the movies data details for the recommendation system in Spark 2.0
  • Exploring the rating data details for the recommendation system in Spark 2.0
  • Building a scalable recommendation engine using collaborative filtering in Spark 2.0
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