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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

Apache Spark SQL

In this chapter, we will examine ApacheSparkSQL, SQL, DataFrames, and Datasets on top of Resilient Distributed Datasets (RDDs). DataFrames were introduced in Spark 1.3, basically replacing SchemaRDDs, and are columnar data storage structures roughly equivalent to relational database tables, whereas Datasets were introduced as experimental in Spark 1.6 and have become an additional component in Spark 2.0.

We have tried to reduce the dependency between individual chapters as much as possible in order to give you the opportunity to work through them as you like. However, we do recommend that you read this chapter because the other chapters are dependent on the knowledge of DataFrames and Datasets.

This chapter will cover the following topics:

  • SparkSession
  • Importing and saving data
  • Processing the text files
  • Processing the JSON files
  • Processing the Parquet files
  • DataSource...
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