PySpark

2nd Edition. — Ramcharan Kakarla, Sundar Krishnan, Balaji Dhamodharan, Venkata Gunnu. — Apress Media LLC., 2024. — 450 p. — ISBN-13: 979-8-8688-0820-3. This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade. In Chapters 1, 2 & 3, we will get...

Manning Publications Co., 2022. — 458 p. — ISBN 978-1617297205. Gustavo Patino, Oakland University William Beaumont School of Medicine Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines. In Data Analysis with Python and PySpark you...

Apress Media LLC, 2024. — 490 p. — ISBN-13: 978-1-4842-9751-3. Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine...

Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills. — O’Reilly Media, 2022. — 215 p. — ISBN-13: 978-1-098-10365-1. The amount of data being generated today is staggering--and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together...

2nd Edition. — Apress Media LLC, 2022. — 230 p. — ISBN-13 (electronic): 978-1-4842-7777-5. Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable Machine Learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins...

Apress, 2019. — 230 p. — ISBN: 978-1-4842-4130-4. Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language...