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Apache Solr for Indexing Data

using built-in analyzers and tokenizers * This step-by-step guide is packed with real-life examples on indexing data Who This Book Is For;

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Mastering Apache Solr 7.x

seamlessly Handle multiple data inputs such as JSON, xml, pdf, doc, xls,ppt, csv and much more. Book DescriptionApache Solr is the only standalone;

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Apache Solr

Build an enterprise search engine using Apache Solr: index and search documents; ingest data from varied sources; apply various text;

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Apache Solr 3.1 Cookbook

This book is part of Packt's Cookbook series; each chapter looks at a different aspect of working with Apache Solr. The recipes deal with;

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Apache Solr High Performance

userfriendly flow, from search optimization using Solr to Deployment of Zookeeper applications. This book is ideal for Apache Solr developers and;

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Solr in Action

DESCRIPTION Whether handling big data, building cloud-based services, or developing multi-tenant web applications, it's vital to;

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Scaling Apache Solr

customers or organization, but have no prior knowledge of Apache Solr/Lucene technologies, this is the book for you.;

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Apache Solr PHP Integration

in integrating search into your applications, this is the book for you. No advanced knowledge of Solr is required. Very basic knowledge of system;

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Next Generation Big Data

enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data;

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Scaling Big Data With Hadoop and Solr

0;...

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Elasticsearch Blueprints

accurate search results, but also improve the quality of searches for relevant results Who This Book Is ForIf you are a data enthusiast and;

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Unimodal And Multimodal Biometric Data Indexing

This work is on biometric data indexing for large-scale identification systems with a focus on different biometrics data indexing methods;

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Modern Data Engineering with Apache Spark

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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Network Data Analytics

In order to carry out data analytics, we need powerful and flexible computing software. However the software available for data analytics;

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The Azure Data Lakehouse Toolkit

Paradigm on Microsoft's Azure cloud platform Benefit from the new Delta Lake open-source storage layer for data lakehouses Take advantage;

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Practical Apache Lucene 8

geospatial data into your application Who This Book Is For Developers wanting to learn the finer details of Apache;

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Practical Apache Lucene 8

data into your application Who This Book Is For Developers wanting to learn the finer details of Apache Lucene by developing a series;

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Mastering Elasticsearch 5.x - Third Edition

Is For If you have some prior working experience with Elasticsearch and want to take your knowledge to the next level, this book will be the;

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Complete Guide to Open Source Big Data Stack

install Mule ESB on a cluster and Cassandra in the cloud Install and use DCOS for big data processing Use Apache Spark for big data stack data;

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Advances in Computers

Enhancements, Eyeing the Patterns: Data Visualization Using Doubly-Seriated Color Heatmaps, Eigenvideo for Video Indexing. Contains novel;

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Scala Programming for Big Data Analytics

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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Beginning Apache Pig

every business need; the features of Pig Latin such as data types for each load, store, joins, groups, and ordering; how Pig workflows can be;

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Streaming Architecture

best practices for designing a reliable architecture that supports this emerging big-data paradigm.Authors Ted Dunning and Ellen Friedman (Real;

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Building Data Streaming Applications with Apache Kafka

practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with;

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Hadoop Application Architectures

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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Spatial Data Management

support spatial data. It presents indexing approaches for spatial data, with a focus on the R-tree. Query evaluation and optimization techniques;

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Beginning Apache Spark Using Azure Databricks

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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