Category: Amazon Neptune

MUFG Bank standardized overseas back-office procedures across roughly 30 countries by grounding generative AI in a structured domain model on Amazon Neptune and Amazon Bedrock. Its AI Flowchart application is expected to cut the system-development effort behind each branch-procedure standardization from 11 person-days to 1, a reduction of up to 90%.

When an Amazon Neptune workload needs resilience beyond a single cluster, this post presents a decision framework that maps availability targets to Neptune architectural patterns, from Multi-AZ and Global Database to write-ahead logs and caching, based on your RTO, RPO, and budget.

Amagi built a Global Metadata Store on Amazon Neptune to model billions of interconnected media metadata relationships. Learn how their RDF knowledge graph, paired with Amazon OpenSearch Service and a custom blank-node synchronization layer, delivers deep semantic queries and sub-500ms transactional reads across 2,500+ channels and 150+ countries.

Learn how to configure Linkurious Enterprise to connect to Amazon Neptune and give business users intuitive, code-free graph exploration for risk investigation, from setup and full-text search with Amazon OpenSearch Service to investigating ultimate beneficial owners for AML compliance.

Learn how to build zero-downtime write architectures for Amazon Neptune using a write queue pattern with Amazon SQS, Amazon Kinesis Data Streams, or Amazon MSK. By decoupling write acceptance from write execution, your application keeps accepting graph writes during maintenance windows, failovers, and scaling operations.

In this post, we show you how to build a semantic ontology that helps your AI assistants navigate enterprise data efficiently. You’ll learn how to structure a property graph store for data relationships, set up vector indexing for semantic search, and implement an automated fact-learning layer that improves use. This bottom-up approach grounds your ontology in the data that exists, building abstractions from observed patterns rather than theoretical models.

In this post, we show how Amazon’s Finance Technology (FinTech) team uses Amazon Neptune to model complex corporate treasury structures as a property graph. These structures include the legal entity relationships, intercompany agreements, and bank account associations that govern payment routing and cash management.

In this post, we discuss the differences between vector search and graph search, how to combine the two for hybrid querying, and use cases that benefit from hybrid querying.

In this post, we walk through how Gremlinq works, demonstrate its capabilities, show you how to set up a Neptune project with the provided templates, and help you understand where this approach might fit in your development context.

This post addresses recovery challenges in multi-database architectures, focusing on both low-consistency and mission-critical scenarios. We explore practical strategies for implementing resilient recovery mechanisms across Amazon DynamoDB, Amazon Aurora, Amazon Neptune, Amazon OpenSearch Service, and other AWS database services.