Aerospike Graph IDE and visualization tool

Organizing data derived via powerful data visualization tools have several real-life applications. Graph visualization and analysis resources allow users to make informed decisions based on actual data.

Here are a few examples of the type of companies or clients that can leverage Aerospike Graph along with the robust data visualization aspect offered by gdotv:

• Data scientists and managers: Visual data analysis allows users to uncover and understand emerging patterns and relationships between data points, discover valuable insights to make informed decisions on ways to transform business processes.
• Cybersecurity groups: Build security graphs to represent protected assets, infrastructure, code dependencies or networks and visualize the connected relationships throughout the organization.
• Business intelligence analysts: Create customized reports and visualizations that can be easily executed for analysts to investigate and communicate on interaction between business data points.
• Fraud detection: Detect fraud patterns by modeling relationships between transactions and financial actors, then interactively visualize graph data to uncover and present potentially suspicious behavior.

Getting started with Aerospike Graph and gdotv is easy. It’s just one simple installation, available on Windows, macOS and Linux. No prerequisites, additional configuration or installation required. You can get started in seconds.

gdotv automatically detects your connection setting requirements based on your database hostname and prompts you for the required information for an easy onboarding experience.

Want a step-by-step walkthrough? Make sure to check out our demo, Get Started with Aerospike Graph + gdotv on YouTube.

Aerospike Graph uses the Gremlin query language. Gremlin, being a traversal language, allows you to navigate the nodes and edges of your graph, filter them by their properties, and perform complex aggregations and pattern-matching operations to extract valuable insights.

It has a unique design that breaks from the convention of established languages such as SQL to provide a development experience tailored to the interconnected nature of graphs.

As an open source framework, Apache TinkerPop benefits from the support of a large community of users, vendors and contributors that build on top of the project to provide enterprise-ready graph database implementations, complete with features such as index management and storage backend configuration.

gdotv Desktop runs locally on your device – no deployment needed and no handing over your data to a third-party system. Everything happens within your organisation’s network, and we have no access to your data. gdotv Web version runs within your own environment, providing the same security benefits.

It’s easy to install and configure too: our setup wizard gets you connected to your graph database in seconds.

Graph databases typically fall into two models: Labeled Property Graph (LPG) and Resource Description Framework (RDF).

The LPG model covers a broad class of databases, including Aerospike Graph, that use nodes, edges, properties and labels to represent graphs. They are optimised for real-time use and excel in applications like fraud detection, social network analysis and recommendation algorithms. There are also a wide range of methods to enforce schema compliance, with flexible conventions and definitions.

The RDF model refers to a highly standardized form of data management using global identifiers. Information is stored as a series of directed “triple” statements connecting a subject, predicate and object together. In addition, various languages built on RDF (such as RDFS and OWL) allow the construction of sophisticated ontologies and semantic reasoning. The high level of standardization also simplifies data integration from disparate sources. This makes RDF ideal for applications in large knowledge graph construction and research.

LPGs excel when performance and ease-of-use are a priority. Users typically find LPGs more intuitive, and they can be tailored to specific use-cases quickly and easily without the semantic complexity or steep learning curve of RDF.