Amazon Neptune resources
Getting started with Amazon Neptune
Looking for information on how you can quickly get started on Amazon Neptune? Below are the most important technical documentation guides, user guides, and tutorials to show how you can get started on Neptune in a few steps.
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Practical Gremlin
Practical Gremlin by Kelvin R. Lawrence is a dynamic, "living" tutorial that guides users through the Apache TinkerPop 3 Gremlin graph traversal language using real-world examples and hands-on exercises. Suitable for programmers and data scientists, it starts from first principles—no prior Gremlin or graph experience required—and walks readers from setup to advanced queries via a structured learning path.
The guide starts with foundational concepts and setup instructions, recommending that readers follow along with the Gremlin Console loaded with sample data kelvinlawrence.net. The core educational tool is the “air-routes” dataset—a model of global airline routes among over 3,300 airports with 43,400 routes—used to demonstrate practical traversal, querying, and graph patterns.
Throughout the book, the author introduces increasingly advanced topics—adding vertices and edges, working with serialization formats, exploring Gremlin Server and scalable graph stores like JanusGraph, and even integrating search backends and languages such as Java, Groovy, Python, and Ruby.
The tutorial emphasizes active learning via code samples, best practices, and community contributions on GitHub, making it a practical pathway to mastering graph traversal with Gremlin.
Graph Databases in Action
Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization.
Relationships in data often look far more like a web than an orderly set of rows and columns. Graph databases shine when it comes to revealing valuable insights within complex, interconnected data such as demographics, financial records, or computer networks.
In Graph Databases in Action, experts Dave Bechberger and Josh Perryman illuminate the design and implementation of graph databases in real-world applications. You'll learn how to choose the right database solutions for your tasks, and how to use your new knowledge to build agile, flexible, and high-performing graph-powered applications!
Graph Databases: New Opportunities for Connected Data
Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.
This second edition includes new code samples and diagrams as well as information on new functionality. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.
- Model data with the Cypher query language and property graph model
- Learn best practices and common pitfalls when modeling with graphs
- Plan and implement a graph database solution in test-driven fashion
- Explore real-world examples to learn how and why organizations use a graph database
- Understand common patterns and components of graph database architecture
- Use analytical techniques and algorithms to mine graph database information
Designing and Building Enterprise Knowledge Graphs
This book is a guide to designing and building knowledge graphs from enterprise relational databases in practice. It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people.
The content of this book is applicable to knowledge graphs being built either with property graph or RDF graph technologies. Knowledge graphs are fulfilling the vision of creating intelligent systems that integrate knowledge and data at large scale.
Tech giants have adopted knowledge graphs for the foundation of next-generation enterprise data and metadata management, search, recommendation, analytics, intelligent agents, and more. We are now observing an increasing number of enterprises that seek to adopt knowledge graphs to develop a competitive edge. In order for enterprises to design and build knowledge graphs, they need to understand the critical data stored in relational databases.
How can enterprises successfully adopt knowledge graphs to integrate data and knowledge, without boiling the ocean? This book provides the answers.
