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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

Building analytical data stores using cloud data lakes

In this section, you will explore the advantages afforded by cloud-based data lakes for big data analytics systems, and then understand some of the challenges facing big data analytics systems while leveraging cloud-based data analytics systems. You will also write a few PySpark code examples to experience these challenges first-hand.

Challenges with cloud data lakes

Cloud-based data lakes offer unlimited, scalable, and relatively inexpensive data storage. They are offered as managed services by the individual cloud providers and offer availability, scalability, efficiency, and lower total cost of ownership. This helps organizations accelerate their digital innovation and achieve faster time to market. However, cloud data lakes are object storages that evolved primarily to solve the problem of storage scalability. They weren't designed to store highly structured, strongly typed, analytical data. Given this, there are...

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