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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 structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark’s key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark’s Streaming components and external community packages
The New Hemi engine has an aggressive persona and outstanding performance. Powering the Challenger, Charger, Ram trucks, and other vehicles in the Chrysler lineup, this engine produces at least one horsepower per cubic inch. Unleashed in 2003, it has been offered in 5.7-, 6.1-, 6.2-, and now 6.4-liter displacements. With each successive engine introduction, Chrysler has extracted more performance. And with the launch of the Hellcat and Demon 6.2-liter supercharged engines, Chrysler built the highest horsepower production engines ever made, at 707 hp and 840 hp respectively. This third-generation Hemi carries on a high-performance Chrysler tradition and is considered the most powerful and "buildable" new pushrod V-8 engine on the market today. Mopar engine expert and veteran author Larry Shepard reveals up-to-date modification techniques and products for achieving higher performance. Porting and modifying the stock Hemi heads as well as the best flow characteristics with high lift are revealed. In addition, guidance on aftermarket heads is provided. A supercharger is one of the most cost-effective aftermarket add-ons, and the options and installation are comprehensively covered. Shepard guides you through the art and science of selecting a cam, so you find a cam that meets your airflow needs and performance goals. He details stock and forged crankshafts plus H- and I-beam connecting rods that support the targeted horsepower, so you can choose the best rotating assembly for your engine. In addition, intake manifold and fuel systems, ignition systems, exhaust systems, and more are covered. With this book, you can transform a New Hemi engine into an even more responsive and faster powerplant. You are able to build the engine that suits all your high-performance needs. p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Arial}
Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You’ll Learn Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard Who This Book Is For BI and big data warehouse professionals interested in gaining practical and real-world insight into next-generation big data processing and analytics using Apache Kudu, Impala, and Spark; and those who want to learn more about other advanced enterprise topics
This timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Features: describes the fundamentals of building scalable software systems for large-scale data processing in the new paradigm of high performance distributed computing; presents an overview of the Hadoop ecosystem, followed by step-by-step instruction on its installation, programming and execution; Reviews the basics of Spark, including resilient distributed datasets, and examines Hadoop streaming and working with Scalding; Provides detailed case studies on approaches to clustering, data classification and regression analysis; Explains the process of creating a working recommender system using Scalding and Spark.
Ten years have passed since the original edition of this book was published, but Alfa Romeo enthusiasts everywhere are more active today than ever in preserving, modifying and racing these excellent cars. Throughout this time, the author in true Alfista fashion, never stopped looking for and trying new techniques to increase the power, overall performance and reliability of Alfas and their engines. This book is the result of much research, and also first-hand experience gained through many Alfa rear wheel drive model projects, from the 105 series to the last of the 75 models. There is a lot of completely new information regarding TwinSpark Cylinder head mods, big-brake mods, LSD adjustment procedure, electrical system improvements, plus many flow-bench diagrams, dyno plots, and much more.

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