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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

Chapter 12: Spark SQL Primer

In the previous chapter, you learned about data visualizations as a powerful and key tool of data analytics. You also learned about various Python visualization libraries that can be used to visualize data in pandas DataFrames. An equally important and ubiquitous and essential skill in any data analytics professional's repertoire is Structured Query Language or SQL. SQL has existed as long as the field of data analytics has existed, and even with the advent of big data, data science, and machine learning (ML), SQL is still proving to be indispensable.

This chapter introduces you to the basics of SQL and looks at how SQL can be applied in a distributed computing setting via Spark SQL. You will learn about the various components that make up Spark SQL, including the storage, metastore, and the actual query execution engine. We will look at the differences between Hadoop Hive and Spark SQL, and finally, end with some techniques for improving the performance...

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