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Graph Algorithms Practical Examples in Apache Spark and Neo4j

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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Apache Spark Graph Processing

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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Spark GraphX in Action

difficult and computationally expensive to explore, query, and analyze. GraphX, a powerful graph processing API for the Apache Spark analytics;

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Practical Apache Spark

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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Apache Spark 2.x for Java Developers

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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Apache Spark Quick Start Guide

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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Spark in Action

data sets. But Spark goes much further than other frameworks. By including machine learning and graph processing capabilities, it makes;

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Mastering Apache Spark 2.x -

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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Learning Apache Spark 2

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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Beginning Apache Spark 3

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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Hadoop Application Architectures

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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Building Web Applications with Python and Neo4j

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

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by;

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Practical Data Analysis -

A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book;

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Spark for Data Science

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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Apache Spark for Data Science Cookbook

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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Apache Spark 2.x Cookbook

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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Large-Scale Graph Processing Using Apache Giraph

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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Big Data Analytics

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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Frank Kane's Taming Big Data with Apache Spark and Python

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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Hands-On Deep Learning with Apache Spark

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 Machine Learning Blueprints

Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development;

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Learning Spark SQL

Design, implement, and deliver successful streaming applications, machine learning pipelines and graph applications using Spark SQL API;

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Mastering Machine Learning on AWS

Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key Features Build;

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Neo4j Graph Data Modeling

0;...

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Mastering Spark with R The Complete Guide to LargeScale Analysis and Modeling

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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