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When it comes to data analytics, it pays to think big. PySpark blends the powerful Spark big data processing engine with the Python programming language to provide a data analysis platform that can scale up for nearly any task. Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build lightning-fast pipelines for reporting, machine learning, and other data-centric tasks. No previous knowledge of Spark is required.A great and gentle introduction to spark.
About the Technology
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 book
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 Hadoop-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. By the time you’re done, you’ll be able to write and run incredibly fast PySpark programs that are scalable, efficient to operate, and easy to debug.What's inside
- Packaging your PySpark code
- Managing your data as it scales across multiple machines
- Re-writing Pandas, R, and SAS jobs in PySpark
- Troubleshooting common data pipeline problems
- Creating reliable long-running jobs
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