Name: The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems (en Inglés)
Price: 293060.00 COP
Availability: InStock
Author: Gosnell, Denise ; Broecheler, Matthias
ISBN: 9781492044079

portada The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems (en Inglés)

Formato

Libro Físico

Autor

Denise Gosnell

Matthias Broecheler

Editorial

O'Reilly Media

Año

2020

Idioma

Inglés

N° páginas

420

Encuadernación

Tapa Blanda

Dimensiones

23.3 x 17.8 x 2.2 cm

Peso

0.67 kg.

ISBN13

9781492044079

N° edición

Categorías

Análisis y captura de datos
Data mining: minería de datos
Inteligencia artificial
Visualización de la información

The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems (en Inglés)

Denise Gosnell (Autor) · Matthias Broecheler (Autor) · O'Reilly Media · Tapa Blanda

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Reseña del libro "The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems (en Inglés)"

Graph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together. By working with concepts from graph theory, database schema, distributed systems, and data analysis, you'll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases. You'll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologies Use graph technology to build a Customer 360 application, the most popular graph data pattern today Dive into hierarchical data and troubleshoot a new paradigm that comes from working with graph data Find paths in graph data and learn why your trust in different paths motivates and informs your preferences Use collaborative filtering to design a Netflix-inspired recommendation system