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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

Using file formats for data storage in data lakes

The file format you choose to store data in a data lake is key in determining the ease of data storage and retrieval, query performance, and storage space. So, it is vital that you choose the optimal data format that can balance these factors. Data storage formats can be broadly classified into structured, unstructured, and semi-structured formats. In this section, we will explore each of these types with the help of code examples.

Unstructured data storage formats

Unstructured data is any data that is not represented by a predefined data model and can be either human or machine-generated. For instance, unstructured data could be data stored in plain text documents, PDF documents, sensor data, log files, video files, images, audio files, social media feeds, and more.

Unstructured data might contain important patterns, and extracting these patterns could lead to valuable insights. However, storing data in unstructured format...

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