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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 4: Real-Time Data Analytics

In the modern big data world, data is being generated at a tremendous pace, that is, faster than any of the past decade's technologies can handle, such as batch processing ETL tools, data warehouses, or business analytics systems. It is essential to process data and draw insights in real time for businesses to make tactical decisions that help them to stay competitive. Therefore, there is a need for real-time analytics systems that can process data in real or near real-time and help end users get to the latest data as quickly as possible.

In this chapter, you will explore the architecture and components of a real-time big data analytics processing system, including message queues as data sources, Delta as the data sink, and Spark's Structured Streaming as the stream processing engine. You will learn techniques to handle late-arriving data using stateful processing Structured Streaming. The techniques for maintaining an exact replica...

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