The book is devoted to practical methods of analyzing large amounts of data using the Python language and the Spark framework, it introduces the Spark programming model and the basics of the PySpark open source system. Each chapter describes a separate aspect of data analysis, shows the basics of data processing in PySpark and Python using the example of data cleaning, and details machine learning using Spark. The book will help the reader understand how the entire PySpark pipeline for complex analytics of large data sets works and works: from creating and evaluating models to cleaning, preprocessing and researching data with a special emphasis on production applications. Separate chapters are devoted to image processing and the Spark NLP library. This book doesn't talk about the merits and demerits of PySpark. The book introduces the Spark programming model and the basics of PySpark - the Python API for Spark. However, it does not pretend to serve as a guide to Spark or be a comprehensive guide to all the backstreets of Spark. Nor does it purport to be a handbook of machine learning, statistics, or linear algebra, although many chapters contain little introductory material before their use. This book will help the reader understand how the entire PySpark pipeline for complex analytics of large data sets works and works, which is not only creating and evaluating models, but also cleaning, preprocessing and researching data with a special emphasis on production applications.