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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

Real-time analytics systems architecture

A real-time data analytics system, as the name suggests, processes data in real time. This is because it is generated at the source, making it available for business users with the minimal latency possible. It consists of several important components, namely, streaming data sources, a stream processing engine, streaming data sinks, and the actual real-time data consumers, as illustrated in the following diagram:

Figure 4.1 – Real-time data analytics

The preceding diagram depicts a typical real-time data analytics systems architecture. In the following sections, we will explore each of the components in more detail.

Streaming data sources

Similar to any of the other enterprise decision support Systems, a real-time data analytics system also starts with data sources. Businesses generate data continuously in real time; therefore, any data source used by a batch processing system is also a streaming data source...

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