Graph Data Modeling and Analytics with Neo4j (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"Graph Data Modeling and Analytics with Neo4j" "Graph Data Modeling and Analytics with Neo4j" is an authoritative guide that empowers professionals and enthusiasts to unlock the full potential of graph data in modern applications. Beginning with foundational graph theory, the book meticulously explores core concepts such as vertices, edges, topologies, and essential schema design strategies. Readers are guided through the nuances of graph versus relational modeling, best practices for pattern recognition, and the critical modeling decisions that pave the way for robust analytics across diverse...
"Graph Data Modeling and Analytics with Neo4j"
"Graph Data Modeling and Analytics with Neo4j" is an authoritative guide that empowers professionals and enthusiasts to unlock the full potential of graph data in modern applications. Beginning with foundational graph theory, the book meticulously explores core concepts such as vertices, edges, topologies, and essential schema design strategies. Readers are guided through the nuances of graph versus relational modeling, best practices for pattern recognition, and the critical modeling decisions that pave the way for robust analytics across diverse domains.
Delving deeper, the book provides a comprehensive tour of Neo4j's architecture, deployment modalities, and its powerful Cypher query language. It covers the intricacies of native graph storage, transactional integrity, index optimization, and scaling in distributed environments, including cloud deployments via Neo4j Aura. Readers will master Cypher for complex pattern matching, aggregations, and advanced data manipulations, all while learning to ensure security and performance in enterprise settings.
Moving towards real-world applications, the book offers hands-on insights into graph algorithms, high-availability system design, and enterprise integration workflows. It presents proven methodologies for data ingestion, event-driven architectures, and API-driven graph services, as well as performance engineering, observability, and resource optimization. The final sections explore cutting-edge topics-temporal and geospatial graphs, graph data science integrations, serverless and edge deployments, and the future landscape of graph technology-making this an indispensable reference for anyone seeking to lead in graph-based analytics and data modeling.
"Graph Data Modeling and Analytics with Neo4j" is an authoritative guide that empowers professionals and enthusiasts to unlock the full potential of graph data in modern applications. Beginning with foundational graph theory, the book meticulously explores core concepts such as vertices, edges, topologies, and essential schema design strategies. Readers are guided through the nuances of graph versus relational modeling, best practices for pattern recognition, and the critical modeling decisions that pave the way for robust analytics across diverse domains.
Delving deeper, the book provides a comprehensive tour of Neo4j's architecture, deployment modalities, and its powerful Cypher query language. It covers the intricacies of native graph storage, transactional integrity, index optimization, and scaling in distributed environments, including cloud deployments via Neo4j Aura. Readers will master Cypher for complex pattern matching, aggregations, and advanced data manipulations, all while learning to ensure security and performance in enterprise settings.
Moving towards real-world applications, the book offers hands-on insights into graph algorithms, high-availability system design, and enterprise integration workflows. It presents proven methodologies for data ingestion, event-driven architectures, and API-driven graph services, as well as performance engineering, observability, and resource optimization. The final sections explore cutting-edge topics-temporal and geospatial graphs, graph data science integrations, serverless and edge deployments, and the future landscape of graph technology-making this an indispensable reference for anyone seeking to lead in graph-based analytics and data modeling.
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