# Graph visualization tools for Neptune In addition to the visualization capabilities that are [built into Neptune graph-notebooks](notebooks-visualization.md), you can also use solutions built by AWS partners and third-party vendors for visualizing data stored in Neptune. Sophisticated graph visualization can help data scientists, managers, and other roles in an organization explore graph data interactively, without having to know how to write complex queries. **Topics** + [ # The open-source graph-explorer ](visualization-graph-explorer.md) + [ # Tom Sawyer Software ](visualization-tom-sawyer.md) + [ # Cambridge Intelligence ](visualization-cambridge-intelligence.md) + [ # Graphistry ](visualization-graphistry.md) + [ # metaphacts ](visualization-metaphacts.md) + [ # G.V() graph database client ](gv-tool.md) + [ # Linkurious ](visualization-Linkurious.md) + [ # Graph.Build ](visualization-graph.build.md) # The open-source graph-explorer [Graph-explorer](https://github.com/aws/graph-explorer) is an open-source low-code visual exploration tool for graph data, available under the Apache-2.0 license. It lets you browse either labeled property graphs (LPG) or Resource Description Framework (RDF) data in a graph database without having to write graph queries. Graph-explorer is intended to help data scientists, business analysts, and other roles in an organization explore graph data interactively without having to learn a graph query language. Graph-explorer provides a React-based web application that can be deployed as a container to visualize graph data. You can connect to Amazon Neptune or to other graph databases that provide an Apache TinkerPop Gremlin or SPARQL 1.1 endpoint. + You can quickly see a summary of the data using the faceted filters, or search the data by typing text into the search bar. + You can also interactively explore node and edge connections. You can view node neighbors to see how objects relate to each other, and then drill down to inspect edges and properties visually. + You can also customize the graph layout, colors, icons, and which default properties to display for nodes and edges. For RDF graphs, you can customize namespaces for resource URIs too. + For reports and presentations involving graph data, you can configure and save views you've created in a high-resolution PNG format. You can also download the associated data into a CSV or JSON file for further processing. ## Using graph-explorer in a Neptune graph notebook The easiest way to use graph-explorer with Neptune is in a [Neptune graph notebook](graph-notebooks.md). If you [use Neptune workbench to host a Neptune notebook](graph-notebooks.md#graph-notebooks-workbench), graph-explorer is automatically deployed with the notebook and connected to Neptune. After you have created a notebook, go to the Neptune console to start graph-explorer: 1. Go to **Neptune**. 1. Under **Notebooks**, select your notebook. 1. Under Actions choose **Open Graph Explorer**. ## How to run graph-explorer in Amazon ECS on AWS Fargate and connect to Neptune You can also build the graph-explorer Docker image and run it on a local machine or a hosted service like [Amazon Elastic Compute Cloud (Amazon EC2)](https://aws.amazon.com/ec2/) or [Amazon Elastic Container Service (Amazon ECS)](https://aws.amazon.com/ecs/), as explained in the [Getting Started](https://github.com/aws/graph-explorer#getting-started) section of the read-me in the [graph-explorer GitHub project](https://github.com/aws/graph-explorer). As an example, this section provides step-by-step instructions for running graph-explorer in Amazon ECS on AWS Fargate: 1. Create a new IAM role and attach these policies to it: + [AmazonECSTaskExecutionRolePolicy](https://console.aws.amazon.com/iam/home#/policies/arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy) + [CloudWatchLogsFullAccess](https://console.aws.amazon.com/iam/home#/policies/arn:aws:iam::aws:policy/CloudWatchLogsFullAccess) Keep the role name handy to use in a minute. 1. [Create an Amazon ECS cluster](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/create-cluster-console-v2.html) with the infrastructure set to FARGATE and the following networking options: + `VPC`: set to the VPC where your Neptune database is located. + `Subnets`: set to the public subnets of that VPC (remove all others). 1. Create a new JSON task definition as follows: ``` { "family": "explorer-test", "containerDefinitions": [ { "name": "graph-explorer", "image": "public.ecr.aws/neptune/graph-explorer:latest", "cpu": 0, "portMappings": [ { "name": "graph-explorer-80-tcp", "containerPort": 80, "hostPort": 80, "protocol": "tcp", "appProtocol": "http" }, { "name": "graph-explorer-443-tcp", "containerPort": 443, "hostPort": 443, "protocol": "tcp", "appProtocol": "http" } ], "essential": true, "environment": [ { "name": "HOST", "value": "localhost" } ], "mountPoints": [], "volumesFrom": [], "logConfiguration": { "logDriver": "awslogs", "options": { "awslogs-create-group": "true", "awslogs-group": "/ecs/graph-explorer", "awslogs-region": "{region}", "awslogs-stream-prefix": "ecs" } } } ], "taskRoleArn": "arn:aws:iam::{account_no}:role/{role_name_from_step_1}", "executionRoleArn": "arn:aws:iam::{account_no}:role/{role_name_from_step_1}", "networkMode": "awsvpc", "requiresCompatibilities": [ "FARGATE" ], "cpu": "1024", "memory": "3072", "runtimePlatform": { "cpuArchitecture": "X86_64", "operatingSystemFamily": "LINUX" } } ``` 1. Start a new task using the default settings, except for the following fields: + **Environment** + Compute options => **Launch type** + **Deployment configuration** + Application Type => **Task** + Family => *(your new JSON task definition)* + Revision => *(latest)* + **Networking** + VPC => *(the Neptune VPC you want to connect to)* + Subnets => *(ONLY the public subnets of the VPC– remove all others)* + Security group => **Create a new security group** + Security group name => graph-explorer + Security group description = Security group for access to graph-explorer + Inbound rules for security groups => 1. 80 Anywhere 1. 443 Anywhere 1. Select **Create**. 1. After the task starts, copy the public IP of the running task, and navigate to: `https://(your public IP)/explorer`. 1. Accept risk of using the unrecognized certificate that has been generated, or add it to your key chain. 1. Now you can add a connection to Neptune. Create a new connection, either for a property graph (LPG) or for RDF, and set the following fields: ``` Using proxy server => true Public or Proxy Endpoint => https://(your public IP address) Graph connection URL => https://(your Neptune endpoint):8182 ``` You should now be connected. ## Graph-explorer demonstration This brief video gives you some idea of how you can easily visualize your graph data using graph-explorer: ![\[Graph-explorer text-only demo video\]](http://docs.aws.amazon.com/neptune/latest/userguide/images/graph-explorer.gif) # Tom Sawyer Software [Tom Sawyer Perspectives](https://www.tomsawyer.com/perspectives) is a low-code graph and data visualization and analysis development platform for data stored in Amazon Neptune. Integrated design and preview interfaces and extensive API libraries allow you to create custom, production-quality visualization applications quickly. With a point-and-click designer interface and 30 built-in analytics algorithms, you can design and develop applications to gain insights into data federated from dozens of sources. [Tom Sawyer Graph Database Browser](https://www.tomsawyer.com/graph-database-browser/) makes it easy to visualize and analyze data in Amazon Neptune. You can see and understand connections in your data without extensive knowledge of the query language or schema. You can interact with the data without technical knowledge simply by loading the neighbors of selected nodes and building the visualization in whatever direction you need. You can also take advantage of five unique graph layouts to display the graph in a way that provides the most meaning, and can apply centrality, clustering, and pathfinding analyses to reveal previously unseen patterns. To see an example of Graph Database Browser integration with Neptune, check out [this blog post](https://aws.amazon.com/blogs/database/exploring-scientific-research-on-covid-19-with-amazon-neptune-amazon-comprehend-medical-and-the-tom-sawyer-graph-database-browser/). To get started with a free trial of Graph Database Browser, visit [the AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-dhynqyslzrqr2). ![\[An animated visualization of graph data.\]](http://docs.aws.amazon.com/neptune/latest/userguide/images/2021.06.08.0.PerspectivesIntro.gif) # Cambridge Intelligence [Cambridge Intelligence](https://cambridge-intelligence.com/) provides data visualization technologies for exploring and understanding Amazon Neptune data. The graph visualization toolkits ([KeyLines](https://cambridge-intelligence.com/keylines/) for JavaScript developers and [ReGraph](https://cambridge-intelligence.com/regraph/) for React developers) offer an easy way to build highly interactive and customizable tools for web applications. These toolkits leverage WebGL and HTML5 Canvas for fast performance, they support advanced graph analysis functions, and combine flexibility and scalability with a secure, robust architecture. These SDKs work with both Neptune Gremlin and RDF data. Check out these integration tutorials for [Gremlin data](https://cambridge-intelligence.com/keylines/amazon-neptune/tutorial/), [SPARQL data](https://cambridge-intelligence.com/visualizing-the-amazon-neptune-database-with-keylines/), and [Neptune architecture](https://cambridge-intelligence.com/aws-neptune-regraph-tutorial/). Here is a sample KeyLines visualization: ![\[Sample KeyLines visualization\]](http://docs.aws.amazon.com/neptune/latest/userguide/images/KeyLines-visualiztion.png) # Graphistry [Graphistry](https://www.graphistry.com/) is a visual graph intelligence platform that leverages GPU acceleration for rich visual experiences. Teams can collaborate on Graphistry using a variety of features, from no-code exploration of files and databases, to sharing Jupyter notebooks and Streamlit dashboards, to using the embedding API in your own apps. You can get started with low-coding fully interactive dashboards by simply configuring and launching the [graph-app-kit](https://github.com/graphistry/graph-app-kit#get-started) and modifying just a few lines of code. Check [this blog post](https://aws.amazon.com/blogs/database/enabling-low-code-graph-data-apps-with-amazon-neptune-and-graphistry/) for a walkthrough of creating your first dashboard using Graphistry and Neptune. You can also try the Neptune [PyGraphistry](https://github.com/graphistry/pygraphistry) demo. PyGraphistry is a Python visual graph analytics library for notebooks. Check out [this tutorial notebook](https://github.com/graphistry/pygraphistry/blob/master/demos/demos_databases_apis/neptune/neptune_tutorial.ipynb) for a Neptune PyGraphistry demo. To get started, visit [Graphistry in the AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-ppbjy2nny7xzk). ![\[alt text not found\]](http://docs.aws.amazon.com/neptune/latest/userguide/images/graphistry-visualization.gif) # metaphacts [metaphacts](https://metaphacts.com/) offers a flexible, open platform for describing and querying graph data and for visualizing and interacting with knowledge graphs. Using [metaphactory](https://metaphacts.com/product), you can build interactive web applications such as visualizations and dashboards on top of knowledge graphs in Neptune using the RDF data model. The metaphactory platform supports a low-code development experience with a UI for data loading, a visual ontology modeling interface with OWL and SHACL support, a SPARQL query UI and query catalog, and a rich set of Web components for graph exploration, visualization, search and authoring. Here is a sample metaphactory visualization: ![\[Sample metaphactory visualization\]](http://docs.aws.amazon.com/neptune/latest/userguide/images/metaphactory-visualization.png) The platform is designed for and used productively in engineering, manufacturing, pharma, life Sciences, finance, insurance, and more. To see a sample solution architecture, check out [this blog post](https://aws.amazon.com/blogs/apn/exploring-knowledge-graphs-on-amazon-neptune-using-metaphactory/). To get started with a free trial of metaphactory, visit the [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-2h6qiqogjqe2m). # G.V() graph database client [G.V()](https://gdotv.com/) is an all-in-one graph database client built for developers and data professionals to explore and interact with Amazon Neptune graph data. With G.V(), you can accelerate your graph projects and perform day-to-day graph database tasks more efficiently. It's available through [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-lifzpx4adcwsq) or as a standalone desktop application for Windows, macOS, and Linux. ## Key features ### Query editors The built-in Gremlin and Cypher query editors provide real-time autocomplete, suggestions, and documentation as you type your query. These features are informed by your graph data schema to help you write accurate queries quickly. You can parameterize and save queries as one-click reports, enabling you to build insightful, centralized reporting for your graph database in minutes. ### Data visualization formats To analyze your query results, G.V() offers multiple data visualization formats: + **Graph visualization** for exploring connections and patterns in your data, with an optional underlay for geographical map data + **Table views**, organizing vertices, edges, or tabular query results as rows for a quick comparison of property values + **JSON data format** for developer-friendly output + **Object browser** to navigate your data based on its hierarchical structure, best suited for complex aggregations of information ### Graph data explorer G.V()'s no-code Graph Data Explorer lets you search your graph data with ease using property filters and traverse edges from vertices interactively via the graph view. You can also create, update, and delete vertices, edges, and their properties with a few clicks to modify your graph data without writing full queries. ### Graph data model view The Graph Data Model view provides a detailed visual representation of your labeled property graph schema to help your team understand your data model. ### Amazon Neptune integration G.V() offers in-depth integration with Amazon Neptune, including: + IAM authentication + Neptune API support + Slow Query analysis + Audit Log insights Check out the [G.V() documentation](https://gdotv.com/docs/#amazon-neptune) to learn more. ## Availability and updates G.V() is continuously evolving with new features released monthly. Get started with a free trial of the [desktop](https://gdotv.com/) or [AWS Marketplace version](https://aws.amazon.com/marketplace/pp/prodview-lifzpx4adcwsq) today. ## Product demonstration See G.V() in action: ![\[G.V() product demonstration showing graph visualization and query capabilities\]](https://dl.gdotv.com/images/gdotv-product-introduction.gif) ## Learn more For more information about G.V() features and capabilities, see the [G.V() documentation](https://docs.gdotv.com). # Linkurious [Linkurious](https://linkurious.com/) provides different graph intelligence solutions for both technical and non-technical users and a variety of use cases. [Linkurious Enterprise Explorer](https://linkurious.com/linkurious-enterprise-explorer/) is an off-the-shelf graph visualization and analysis software built for teams that can keep up with the demands of your day-to-day activities and helps the data-driven professionals do big things - simply. Fully configurable and easy to use, it easily adapts to your needs and empowers novices or advanced users to quickly visualize data in AWS Neptune, to intuitively explore your dataset no matter the size or complexity of your data and to seamlessly collaborate at the team or enterprise levels. [Linkurious Enterprise Watchtower](https://linkurious.com/linkurious-enterprise-watchtower/) taps into the power of Linkurious Enterprise Explorer and adds innovative detection and case management capabilities to offer an integrated [detection](https://linkurious.com/linkurious-enterprise-watchtower-detection/) and investigation software powered by graph technology. On one hand, it enables you to configure alerts that leverage Neptune Database and Neptune Analytics to automatically surface anomalies or patterns in complex connected data. On the other hand, it combines [case management and collaboration](https://linkurious.com/linkurious-enterprise-watchtower-collaboration/) features to help teams efficiently manage their investigative workflows. [Ogma](https://linkurious.com/ogma/) is a commercial JavaScript library that helps you develop powerful, large-scale interactive graph visualizations for your applications. It leverages WebGL rendering and high-performance layouts to enable users to display and interact with thousands of nodes and edges in a matter of seconds. It also provides a variety of features to customize your application and create rich user experiences. Finally, it comes equipped with comprehensive [documentation](https://doc.linkurious.com/ogma/latest/) and tools such as [tutorials](https://doc.linkurious.com/ogma/latest/tutorials/styling/), dozens of [examples](https://doc.linkurious.com/ogma/latest/examples/transport-network.html) and an interactive [playground](https://doc.linkurious.com/ogma/latest/playground/index.html). To get started, request a [ 30-day free trial](https://resources.linkurious.com/lke-free-trial) of Linkurious Enterprise or Ogma. # Graph.Build In any domain, collaboration with subject matter experts is key to designing graph models that effectively address specific use cases. Whether you're tackling traditional graph workloads like graph analytics or exploring advanced AI applications such as GraphRAG, link prediction, or neuro-symbolic reasoning, the ability to build and iterate on graph models is essential. With Graph.Build, designing and refining graph models becomes more accessible, empowering domain experts to work hand-in-hand with engineers and data analysts to create impactful, tailored solutions. The [Graph.Build](https://graph.build/) platform is a dedicated, no-code graph model design studio and build factory. Graph.Build is available in the [AWS Marketplace](https://aws.amazon.com/marketplace/seller-profile?id=778d246b-80cd-4728-9fbf-31cc3e1cc182). ## Graph.Build Studio: Studio is a no-code, collaborative graph design IDE. You can use the studio to design schemas for LPG graphs, Ontologies for RDF graphs, ETL models, Change Data Capture configurations, and even virtual SPARQL graph endpoints. ## Graph.Build Transformers: Transformers create graph models from real data. Transformer graph models can be designed in the Studio, meaning that designing, building and testing graph models requires no code. You can connect transformers to a wide range of data sources including [Amazon Aurora](https://aws.amazon.com/rds/aurora/), [Amazon Athena](https://aws.amazon.com/athena/), [Amazon S3](https://aws.amazon.com/s3/) and [Amazon MSK](https://aws.amazon.com/msk/). Transformers have a wide range of features for maintaining graph models found in other datasets, including change data capture (CDC), virtualization and materialization in update or insert mode. ## Graph.Build Writers: Writers are configured to write or update both Labelled Property Graph(LPG) and Resource Description Framework(RDF) graph models in target graph databases, like Amazon Neptune and Neptune Analytics. ![\[Ontology model diagram for air routes, showing relationships between country, city, airport, airline, route, and plane entities.\]](http://docs.aws.amazon.com/neptune/latest/userguide/images/graph-build-screenshot.png) [Request a demo](https://graph.build/enquire/book-a-demo) today.