دانلود کتاب تحلیل داده با پایتون و PySpark

While computers have been getting more powerful and more capable of chewing though larger data sets, our appetite for consuming data grows much faster. Consequently, we built new tools to scale big data jobs across multiple machines. This does not come for free, and early tools were complicated by requiring users to manage not only the data program, but also the health and performance of the cluster of machines themselves. I recall trying to scale my own programs, only to be faced with the advice to “just sample your data set and get on with your day.”
PySpark changes the game. Starting with the popular Python programming language, it provides a clear and readable API to manipulate very large data sets. Still, while in the driver’s seat, you write code as if you were dealing with a single machine. PySpark sits at the intersection of powerful, expressive, and versatile. Through a powerful multidimensional data model, you can build your data programs with a clear path to scalability, regardless of the data size.