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Large-Scale Graph Processing Using Apache Giraph

practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage;

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Hadoop Application Architectures

using windowing analytics Giraph, GraphX, and other tools for large graph processing on Hadoop Using workflow orchestration and scheduling tools;

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Apache Spark Graph Processing

. * Build and analyze large-scale network datasets * Analyze and transform graphs using RDD and graph-specific operations * Implement new custom;

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Spark GraphX in Action

While graphs are often the most natural way to represent the connections among data, the complexity of large graphs makes them conceptually;

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Apache Spark 2.x Cookbook

engine using MLlib * Graph processing using GraphX and GraphFrames libraries * Develop a set of common applications or project types, and;

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Mastering Apache Spark 2.x -

* Study highly optimised unified batch and real-time data processing using SparkSQL and Structured Streaming * Evaluate large-scale Graph;

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

analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats;

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Apache Spark 2.x for Java Developers

analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages. By the end of the book, you will have a solid;

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Creativity in Load-Balance Schemes for Multi/Many-Core Heterogeneous Graph Computing

decomposition, accelerated network traversal using multi/many-cores, partitioning of complex networks for heterogeneous computing, topology-aware load;

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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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Spark in Action

graph-shaped data using Spark GraphX, and an introduction to Spark clustering. Key Features: * Clear introduction to Spark;

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Apache Spark for Data Science Cookbook

-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. * Learn about;

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Large scale Graph Analysis System Algorithm and Optimization

This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and;

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Large scale Graph Analysis System Algorithm and Optimization

This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and;

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Large Scale and Big Data

techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big;

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Large Scale and Big Data

techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big;

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Mastering Apache Storm

using Apache Storm, then this book is for you. No previous experience in Storm is required as this book starts from the basics. After finishing;

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

in a book. Understand and analyze large data sets using Spark on a single system or on a cluster. About This Book * Understand how Spark;

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Programming Elastic MapReduce

to get started. This practical guide shows you how to quickly launch data analysis projects in the cloud by using Amazon Elastic MapReduce;

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Apache Hadoop 3 Quick Start Guide

efficiently processed instead of using one large computer to store and process the data. This book will get you started with the Hadoop ecosystem, and;

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Apache Hadoop YARN

This book is a critically needed resource for the newly released Apache Hadoop 2.0, highlighting YAR;...

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Mahout in Action

HIGHLIGHT The first and only book on Apache Mahout, an open source tool for leveraging machi;...

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Apache Spark 2

to optimize your work by using many features of Spark 2.x and Scala Book DescriptionApache Spark is an in-memory, cluster-based data;

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Learning Apache Spark 2

* Exclusive guide that covers how to get up and running with fast data processing using Apache Spark * Explore and exploit various possibilities with;

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An Architecture for Fast and General Data Processing on Large Clusters

required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming;

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An Architecture for Fast and General Data Processing on Large Clusters

required of data processing have grown. In addition to simple queries, complex algorithms like machine learning and graph analysis are becoming;

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Beginning Apache Spark Using Azure Databricks

Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running;

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