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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 2. Apache Spark SQL FREE CHAPTER 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

Structured Streaming

As you might already have understood from the previous chapters, Apache Spark is currently in transition from RDD-based data processing to a more structured one, backed by DataFrames and Datasets in order to let Catalyst and Tungsten kick in for performance optimizations. This means that the community currently uses a double-tracked approach. While the unstructured APIs are still supported--they haven't even been marked as deprecated yet ,and it is questionable if they ever will--a new set of structured APIs has been introduced for various components with Apache Spark V 2.0, and this is also true for Spark Streaming. Structured Steaming was marked stable in Apache Spark V 2.2. Note that, as of Apache Spark V 2.1 when we started writing this chapter, Structured Streaming is was marked as alpha. This is another example of the extreme pace at which Apache...

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