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and improving machine learning predictions. You'll walk through hands-on examples that show you how to use graph algorithms in Apache Spark;
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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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difficult and computationally expensive to explore, query, and analyze. GraphX, a powerful graph processing API for the Apache Spark analytics;
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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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blend of theory and practical examples, and is written in a way that will gradually build your knowledge of Apache Spark.;
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architecture and the execution flow of a Spark application Explore built-in modules for SQL, streaming, ML, and graph analysis Optimize your Spark job;
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data sets. But Spark goes much further than other frameworks. By including machine learning and graph processing capabilities, it makes;
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of instructions and practical examples to extend the most up-to date Spark functionalities. * Extend your data processing capabilities to process huge;
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capabilities in the form of Spark SQL, Spark Streaming, Spark ML and Graph X all accessible via Java, Scala, Python and R. Deploying the key;
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learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples;
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such as Apache Oozie Near-real-time stream processing with Apache Storm, Apache Spark Streaming, and Apache Flume Architecture examples for;
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Is ForIf you are a Python developer and want to expand your understanding of Python-based web applications over Neo4j graph data models, this;
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Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by;
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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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the fast and scalable computing features of Spark to address big data challenges * Work through practical examples on real-world problems;
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system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. * Get hands;
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Over 70 recipes to help you use Apache Spark as your single big data computing platform and master its libraries About This Book;
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students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples;
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Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get;
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over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement;
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Speed up the design and implementation of deep learning solutions using Apache Spark Key Features Explore the world of distributed deep;
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Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development;
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Design, implement, and deliver successful streaming applications, machine learning pipelines and graph applications using Spark SQL API;
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Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key Features Build;
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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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