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Mastering Apache Spark 2.x

You're reading from   Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (15) Chapters Close

Preface 1. A First Taste and What’s New in Apache Spark V2 FREE CHAPTER 2. Apache Spark SQL 3. The Catalyst Optimizer 4. Project Tungsten 5. Apache Spark Streaming 6. Structured Streaming 7. Apache Spark MLlib 8. Apache SparkML 9. Apache SystemML 10. Deep Learning on Apache Spark with DeepLearning4j and H2O 11. Apache Spark GraphX 12. Apache Spark GraphFrames 13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience 14. Apache Spark on Kubernetes

Summary


Our continuing theme when examining both Apache Hadoop and Spark is that none of these systems stand alone. They need to be integrated to form ETL-based processing systems. Data needs to be sourced and processed in Spark and then passed to the next link in the ETL chain or stored. We hope that this chapter showed you that Spark functionality can be extended with extra libraries and systems such as H2O, DeepLearning4j. Even Apache SystemML supports DeepLearning now and TensorFlow can be run within Apache Spark using TensorFrames and TensorSpark.

Although Apache Spark MLlib and SparkML has a lot of functionality, the combination of H2O Sparkling Water and the Flow web interface provides an extra wealth of data analysis modeling options. Using Flow, you can also visually and interactively process your data. We hope that this chapter shows you, even though it cannot cover all that all these libraries offer, that the combination of Spark and third-party libraries widens your data processing...

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