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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 have explored how you can take advantage of Apache Spark's Thrift server to enable JDBC/ODBC connectivity and use Apache Spark as a distributed SQL engine. You learned how the HiveServer2 service allows external tools to connect to Apache Hive using JDBC/ODBC standards and how Spark Thrift Server extends HiveServer2 to enable similar functionality on Apache Spark clusters. Steps required for connecting SQL analysis tools such as SQL Workbench/J were presented in this chapter, along with detailed instructions required for connecting BI tools such as Tableau Online with Spark clusters. Finally, steps required for connecting arbitrary Python applications, either locally on your machine or on remote servers in the cloud or a data center, to Spark clusters using Pyodbc were also presented. In the following and final chapter of this book, we will explore the Lakehouse paradigm that can help organizations seamlessly cater to all three workloads of data analytics...

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