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Mastering Apache Spark 2.x - Second Edition

You're reading from  Mastering Apache Spark 2.x - Second Edition

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Pages 354 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (21) Chapters close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. A First Taste and What’s New in Apache Spark V2 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

Spark SQL


From Spark version 1.3, data frames have been introduced in Apache Spark so that Spark data can be processed in a tabular form and tabular functions (such as select, filter, and groupBy) can be used to process data. The Spark SQL module integrates with Parquet and JSON formats to allow data to be stored in formats that better represent the data. This also offers more options to integrate with external systems.

The idea of integrating Apache Spark into the Hadoop Hive big data database can also be introduced. Hive context-based Spark applications can be used to manipulate Hive-based table data. This brings Spark's fast in-memory distributed processing to Hive's big data storage capabilities. It effectively lets Hive use Spark as a processing engine.

Additionally, there is an abundance of additional connectors to access NoSQL databases outside the Hadoop ecosystem directly from Apache Spark. In Chapter 2, Apache Spark SQL, we will see how the Cloudant connector can be used to access a remote ApacheCouchDB NoSQL database and issue SQL statements against JSON-based NoSQL document collections.

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Mastering Apache Spark 2.x - Second Edition
Published in: Jul 2017 Publisher: Packt ISBN-13: 9781786462749
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