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What is Graph Story?

Organizations opting for a do-it-yourself approach to implement a graph database should expect a preparation period of approximately 2 to 3 months before reaching a production-ready status. On the other hand, utilizing Graph Story’s managed services can enable you to set up your operational database in mere minutes. You can uncover a range of graph use cases while also examining a comparative analysis between self-hosting and managed services. We support deployments within your current infrastructure, whether it is hosted on AWS, Azure, or Google Compute Engine, and we can accommodate any geographical area. If you need VPC peering or specific IP access restrictions, we can swiftly tailor our services to meet those requirements. For those interested in developing a proof of concept, launching a single enterprise graph instance can be accomplished in just a few clicks. If your needs shift towards scaling up to a high-availability production cluster, we are ready to provide support! Our graph database management tools are crafted to enhance your experience, allowing for easy monitoring of CPU, memory, and disk usage. Furthermore, you will have access to crucial configurations, logs, and the ability to back up your database and restore snapshots as needed. This degree of adaptability guarantees that your graph database management meets your operational needs seamlessly. Additionally, our support team is always available to help troubleshoot any issues, ensuring a smooth experience throughout your database management journey.

What is Apache TinkerPop?

Apache TinkerPopâ„¢ is a dynamic graph computing framework that caters to both online transaction processing (OLTP) in graph databases and online analytical processing (OLAP) within graph analytic systems. At the heart of this framework lies Gremlin, a robust graph traversal language that empowers users to craft complex queries and traversals on their application's property graph with finesse. Each traversal in Gremlin comprises a sequence of steps that can be nested, offering significant flexibility in how data is explored and analyzed. Fundamentally, a graph is formed by interconnected vertices and edges, each capable of containing various key/value pairs referred to as properties. Vertices represent unique entities such as people, places, or events, while edges denote the relationships that link these vertices together. For instance, a vertex could signify an individual who knows another person, attended a specific event, or visited a certain place recently. This framework proves especially advantageous when tackling intricate domains filled with diverse objects (vertices) that can be linked through various types of relationships (edges). By grasping this structural design, users can maximize the potential of their data and extract meaningful insights from their interconnected networks. Ultimately, the ability to navigate and analyze such complex relationships enhances decision-making processes and drives innovation across various fields.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Google Compute Engine
Microsoft Azure

Integrations Supported

Apache Groovy
Docker
G.V() Gremlin IDE
Java
Node.js
Python

API Availability

Has API

API Availability

Has API

Pricing Information

$299 per month

Pricing Information

Free
Free Version

Supported Platforms

SaaS
On-Prem

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Graph Story

Date Founded

2014

Company Location

United States

Company Website

www.graphstory.com

Company Facts

Organization Name

Apache Software Foundation

Company Location

United States

Company Website

tinkerpop.apache.org

Categories and Features

Graph Databases

Not specified

Categories and Features

Graph Databases

Not specified

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