Name: High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (en Inglés)
Price: 44570.00 CLP
Availability: InStock
Author: Karau, Holden ; Warren, Rachel
ISBN: 9781491943205

portada High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (en Inglés)

Formato

Libro Físico

Autor

Holden Karau

Rachel Warren

Editorial

O'Reilly Media

Idioma

Inglés

Encuadernación

Tapa Blanda

Dimensiones

23.4 x 17.8 x 1.8 cm

Peso

0.54 kg.

ISBN13

9781491943205

Categorías

Programación informática - desarrollo de software
Data warehousing
Data mining: minería de datos

High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (en Inglés)

Holden Karau (Autor) · Rachel Warren (Autor) · O'Reilly Media · Tapa Blanda

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Origen: Estados Unidos (Costos de importación incluídos en el precio)

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Reseña del libro "High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark (en Inglés)"

Apache Spark is amazing when everything clicks. But if you haven't seen the performance improvements you expected, or still don't 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, you'll also learn how to make it sing.With this book, you'll explore: How Spark SQL's new interfaces improve performance over SQL's 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 Spark's 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 librariesSpark's Streaming components and external community packages