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Leverage deep and machine learning models to build applications from real-time data using PySpark. This book is perfect for those who want to learn how to use this language to perform exploratory data analysis and solve a variety of business challenges. You'll start by reviewing PySpark fundamentals, such as the core Spark architecture, and see how to use PySpark for big data processing, such as data ingestion, cleansing, and transformation techniques. This is followed by creating workflows to analyze streaming data using PySpark and a comparison of various streaming platforms. Next, you'll see how to schedule different Spark jobs using Airflow with PySpark and examine the tuning machine and deep learning models for real-time predictions. This book concludes with a discussion of graph frameworks and performing network analysis using graph algorithms in PySpark. All code presented in the book will be available in Python scripts on Github. What you'll learn Develop pipelines for processing streaming data using PySpark Build machine learning and deep learning models using the latest PySpark offerings Use graph analysis with PySpark Create sequence embeds from text data Who is this book for?
Data scientists, machine learning and deep learning engineers who want to learn and use PySpark for real-time analysis of streaming data.
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