Fast Data Processing with Spark
By Holden Karau
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About this ebook
Spark is a framework for writing fast, distributed programs. Spark solves similar problems as Hadoop MapReduce does but with a fast in-memory approach and a clean functional style API. With its ability to integrate with Hadoop and inbuilt tools for interactive query analysis (Shark), large-scale graph processing and analysis (Bagel), and real-time analysis (Spark Streaming), it can be interactively used to quickly process and query big data sets.
Fast Data Processing with Spark covers how to write distributed map reduce style programs with Spark. The book will guide you through every step required to write effective distributed programs from setting up your cluster and interactively exploring the API, to deploying your job to the cluster, and tuning it for your purposes.
Fast Data Processing with Spark covers everything from setting up your Spark cluster in a variety of situations (stand-alone, EC2, and so on), to how to use the interactive shell to write distributed code interactively. From there, we move on to cover how to write and deploy distributed jobs in Java, Scala, and Python.
We then examine how to use the interactive shell to quickly prototype distributed programs and explore the Spark API. We also look at how to use Hive with Spark to use a SQL-like query syntax with Shark, as well as manipulating resilient distributed datasets (RDDs).
ApproachThis book will be a basic, step-by-step tutorial, which will help readers take advantage of all that Spark has to offer.
Who this book is forFast Data Processing with Spark is for software developers who want to learn how to write distributed programs with Spark. It will help developers who have had problems that were too much to be dealt with on a single computer. No previous experience with distributed programming is necessary. This book assumes knowledge of either Java, Scala, or Python.
Holden Karau
Holden Karau is a queer transgender Canadian, Apache Spark committer, Apache Software Foundation member, and an active open source contributor. She also extends her passion for building community with industry projects including Scaling for Python for ML and teaching distributed computing to children. As a software engineer, she's worked on a variety of distributed compute, search, and classification problems at Google, IBM, Alpine, Databricks, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor of mathematics in computer science. Outside of software she enjoys playing with fire, welding, riding scooters, eating poutine, and dancing.
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Reviews for Fast Data Processing with Spark
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- Rating: 1 out of 5 stars1/5O livro é muito mal escrito, mal elaborado e com pouco conteúdo. Não recomendo.