AWS Neptune Visualization with Graphlytic

AWS Neptune is a fully managed graph database service from Amazon Web Services, purpose-built for high-performance graph applications. It supports two property graph query languages — Gremlin and openCypher — as well as SPARQL for RDF workloads. Neptune's managed nature, with automatic backups, multi-AZ replication, and VPC-isolated networking, makes it a common choice for teams building graph applications on AWS who want to avoid database operations overhead.

Neptune handles storage and query execution efficiently, but interactive graph exploration still requires either writing traversal queries or building custom tooling. Graphlytic provides a visual front end that connects to Neptune's Gremlin endpoint, turning your managed graph database into an explorable workspace for analysts, investigators, and data teams.

How Graphlytic connects to AWS Neptune

Graphlytic connects to Neptune using the Gremlin WebSocket protocol over TLS. You configure the Neptune cluster endpoint (writer endpoint or load balancer), port (typically 8182), and the AWS region. Because Neptune is VPC-isolated, Graphlytic Server must be deployed within the same VPC — the typical options are an EC2 instance in the same VPC, an SSH tunnel from your network, or an Application Load Balancer (ALB) with a self-signed certificate in front of the Neptune cluster.

Important: Graphlytic does not currently support Neptune IAM database authentication. IAM auth must be disabled on your Neptune cluster for Graphlytic to connect. Neptune does not use username/password authentication — Graphlytic connects without credentials once the VPC network access is in place. Schema auto-detection reads vertex labels, edge labels, and property keys from your Neptune graph to populate the visual interface.

Why visualize Neptune data with Graphlytic

Query templates for non-technical users

Neptune's Gremlin and openCypher interfaces are powerful but require query expertise. Graphlytic lets data engineers save traversal queries as parameterized templates that analysts can run through a form interface — selecting an account ID, a date range, or a risk threshold without writing any query code. The full query runs against Neptune; the result renders in the visual canvas.

Incremental graph exploration

Neptune graphs can be large. Graphlytic loads graph data in query-scoped batches — start from a seed vertex and expand its neighborhood incrementally. This approach is practical for Neptune deployments where full graph loads would be too slow or expensive to execute. Each expansion step sends a targeted Gremlin query to Neptune and renders the result visually.

Investigation collaboration

Saved graph workspaces let investigators share their findings with colleagues. Named snapshots of a graph exploration state — which vertices are visible, how they're styled, which filters are active — can be shared within a Graphlytic project. This makes it practical to hand off an investigation, present findings, or return to previous work.

VPC-native deployment

Because Neptune only accepts connections from within the same VPC, Graphlytic Server is deployed inside your AWS environment as well. This keeps all graph traffic within the AWS network, satisfies data residency requirements, and simplifies security group configuration. Graphlytic can run on EC2, ECS, or any compute resource with VPC connectivity to the Neptune cluster. If you need to reach Neptune from outside the VPC, an SSH tunnel or an ALB-fronted endpoint are the supported options.

Use cases with AWS Neptune and Graphlytic

• Financial crime and fraud detection — trace transaction paths, identify money mule networks, and visualize fraud ring structures in Neptune-backed financial graphs. See anti-fraud use case.
• Identity graphs and customer 360 — explore relationships between customer identities, devices, accounts, and behaviors stored in Neptune
• Cloud infrastructure topology — map AWS resource dependencies, IAM permission relationships, and network connectivity. See infrastructure use case.
• Supply chain risk — model supplier relationships, geographic exposures, and dependency chains in Neptune; visualize disruption propagation paths
• Knowledge graphs and semantic search — explore entity relationship graphs built for search augmentation or recommendation systems

Advantages of Graphlytic with AWS Neptune

• Fully managed Neptune — no database operations; Neptune handles backups, patching, and multi-AZ failover
• VPC-native deployment — Graphlytic runs inside your VPC alongside Neptune, keeping all data within your AWS environment
• Standard Gremlin protocol — connects using the same Gremlin WebSocket interface used by other Neptune clients
• Multiple VPC access patterns — EC2 in the same VPC, SSH tunnel, or ALB-fronted endpoint depending on your network topology
• Scales to large graphs — query-scoped loading keeps visualization responsive at Neptune scale

See the Graph Connections documentation for AWS Neptune endpoint configuration and VPC setup guidance.