Böcker av Holden Karau

Modern systems contain multi-core CPUs and GPUs that have the potential for parallel computing. But many scientific Python tools were not designed to leverage this parallelism. With this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn. Authors Holden Karau and Mika Kimmins show you how to use Dask computations in local systems and then scale to the cloud for heavier workloads. This practical book explains why Dask is popular among industry experts and academics and is used by organizations that include Walmart, Capital One, Harvard Medical School, and NASA. With this book, you'll learn: What Dask is, where you can use it, and how it compares with other tools How to use Dask for batch data parallel processing Key distributed system concepts for working with Dask Methods for using Dask with higher-level APIs and building blocks How to work with integrated libraries such as scikit-learn, pandas, and PyTorch How to use Dask with GPUs.

Apache Spark is amazing when everything clicks. But if you havent seen the performance improvements you expected, or still dont feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources.Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, youll also learn how to make it sing.With this book, youll explore:How Spark SQLs new interfaces improve performance over SQLs RDD data structureThe choice between data joins in Core Spark and Spark SQLTechniques for getting the most out of standard RDD transformationsHow to work around performance issues in Sparks key/value pair paradigmWriting high-performance Spark code without Scala or the JVMHow to test for functionality and performance when applying suggested improvementsUsing Spark MLlib and Spark ML machine learning librariesSparks Streaming components and external community packages