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Essential PySpark for Scalable Data Analytics

You're reading from   Essential PySpark for Scalable Data Analytics A beginner's guide to harnessing the power and ease of PySpark 3

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Length 322 pages
Edition 1st Edition
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Author (1):
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Sreeram Nudurupati Sreeram Nudurupati
Author Profile Icon Sreeram Nudurupati
Sreeram Nudurupati
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Data Engineering
2. Chapter 1: Distributed Computing Primer FREE CHAPTER 3. Chapter 2: Data Ingestion 4. Chapter 3: Data Cleansing and Integration 5. Chapter 4: Real-Time Data Analytics 6. Section 2: Data Science
7. Chapter 5: Scalable Machine Learning with PySpark 8. Chapter 6: Feature Engineering – Extraction, Transformation, and Selection 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Machine Learning Life Cycle Management 12. Chapter 10: Scaling Out Single-Node Machine Learning Using PySpark 13. Section 3: Data Analysis
14. Chapter 11: Data Visualization with PySpark 15. Chapter 12: Spark SQL Primer 16. Chapter 13: Integrating External Tools with Spark SQL 17. Chapter 14: The Data Lakehouse 18. Other Books You May Enjoy

Summary

In this chapter, you learned about SQL as a declarative language that has been universally accepted as the language for structured data analysis because of its ease of use and expressiveness. You learned about the basic constructions of SQL, including the DDL and DML dialects of SQL. You were introduced to the Spark SQL engine as the unified distributed query engine that powers both Spark SQL and DataFrame APIs. SQL optimizers, in general, were introduced, and Spark's very own query optimizer Catalyst was also presented, along with its inner workings as to how it takes a Spark SQL query and converts it into Java JVM bytecode. A reference to the Spark SQL language was also presented, along with the most important DDL and DML statements, with examples. Finally, a few performance optimizations techniques were also discussed to help you get the best out of Spark SQL for all your data analysis needs. In the next chapter, we will extend our Spark SQL knowledge and see how external...

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