When it comes to data analytics, it pays tothink big. PySpark blends the powerful Spark big data processing engine withthe Python programming language to provide a data analysis platform that can scaleup for nearly any task. Data Analysis with Python and PySpark is yourguide to delivering successful Python-driven data projects.

Data Analysis with Python and PySpark is a carefully engineered tutorial that helps you use PySpark to deliver your data-driven applications at any scale. This clear and hands-on guide shows you how to enlarge your processing capabilities across multiple machines with data from any source, ranging from Had oop-based clusters to Excel worksheets. You'll learn how to break down big analysis tasks into manageable chunks and how to choose and use the best PySpark data abstraction for your unique needs.

The Spark data processing engine is an amazing analytics factory: raw data comes in,and insight comes out. Thanks to its ability to handle massive amounts of data distributed across a cluster, Spark has been adopted as standard by organizations both big and small. PySpark, which wraps the core Spark engine with a Python-based API, puts Spark-based data pipelines in the hands of programmers and data scientists working with the Python programming language. PySpark simplifies Spark's steep learning curve, and provides a seamless bridge between Spark and an ecosystem of Python-based data science tools.

About the Author

As a data scientist for an engineering consultancy Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts.

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