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efficiently combining Python and Apache Spark to process data Book DescriptionApache Spark is an open source framework for efficient cluster computing;
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Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book * Use Apache Spark for data processing;
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and use-cases of Apache Spark 2.x, this Learning Path is ideal for you. Big data professionals who want to learn how to integrate and use the;
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This book is for data engineers, data scientists, and those who want to implement Spark for real-time data processing. Anyone who is using;
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workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book;
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Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable;
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A handy reference guide for data analysts and data scientists to help to obtain value from big data analytics using Spark on Hadoop;
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Book Is For Readers who have a basic knowledge of big data systems and want to advance their knowledge with hands-on recipes. What You;
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over 15 real-world examples teaching you Big Data processing with Spark Who This Book Is For If you are a data scientist or data analyst who;
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data processing with Spark-without having to learn Scala! * Use the Spark Java API to implement efficient enterprise-grade applications for;
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with sample code snippets Who This Book Is For This book is for anyone who wants to leverage Apache Spark for data science and machine;
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parameter settings affect overall performance of an Apache Spark cluster * Leverage Scala, R and python for your data science projects In Detail;
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A practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark. Key;
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efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing;
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in comparison to Java, and how Scala is related to Apache Spark for big data analytics. Next, you'll set up the Scala environment ready for examining;
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of practical code snippets for each topic. Practical Apache Spark also covers the integration of Apache Spark with Kafka with examples. You'll follow a;
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the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics;
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of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large;
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improve and refine your predictive models for practical implementation Who This Book Is For If you are a data scientist, a data analyst, or;
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Build, process and analyze large-scale graph data effectively with Spark About This Book * Find solutions for every stage of data;
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Who this book is forIf you are a Scala developer, data scientist, or data analyst who wants to learn how to use Spark for implementing;
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Intermediate-Advanced user level;...
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A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book;
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analytics and its importance for organizations and data professionals * Delve into Spark to see how it is different from existing processing;
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. Apache Spark is a big data processing framework perfect for analyzing near-real-time streams and discovering historical patterns in batched;
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the process.This book covers: Factors to consider when using Hadoop to store and model data Best practices for moving data in and out of the;
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difficult and computationally expensive to explore, query, and analyze. GraphX, a powerful graph processing API for the Apache Spark analytics;
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