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What is a Graph Database?

What is a Graph Database?

Graph databases are database management systems that store information as logical nodes and edges, representing information as a connected graph rather than tables and columns. The graph database system then offers graph query languages, APIs, and other interfaces to query, visualize, and manage data visible in this graph representation. 

If you’ve ever drawn a mind map or flow chart on a whiteboard, you were sketching out a graph data model. If you’ve seen knowledge boxes next to Google search results, found a product on Amazon, or ordered a ride via Uber, you’ve been interacting with a graph database!

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Why use a graph database?

You couldn’t represent all of the data you work with in a single table, could you? Even someone adept in Excel will find trouble. They’ll use every trick in the book, from lookup tables to stored functions, to try and relate information that comes from a variety of sources and relates in multiple ways. Many of the applications that power the apps we love, and the systems that we all depend on, require robust interconnected relationships that get harder and harder to build in a traditional approach.

The graph database is here to provide those solutions. Stardog is an Enterprise Knowledge Graph Platform and the best choice on the market to relate all of these data points and enable rapid development of the next generation of applications and system integrations.

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“Data and analytics professionals often struggle to distinguish between different graph implementation models. However, the traditional split between RDF and property graph support is becoming less important for graph DBMS selection than features that support enterprise readiness." Merv Adrian, Afraz Jaffri 30 August 2022, Gartner Market Guide for Graph Database Management Systems

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About Types of Graph Databases

Database literature and trade materials talk of different types of graph databases: RDF or semantic database, labeled property graphs, etc. And then there is the explanation of graph verses relational databases. 

However, Stardog simplifies the story with the full support of all forms of graph modeling types. We eliminate the either-or analysis and allow users to focus on what capabilities best match the use case at hand. Stardog enables both complete semantic knowledge graphs but also can build any form of graph, including adding property graphs with our edge properties.

Data is more connected than ever. While we want to enable building graph applications where all information can be loaded into a graph, there are some considerations. The value of the graph increases as more important connections get added. And moving or copying data may be prohibited or unfeasible. Thanks to our data virtualization technology, Stardog can extend the graph model as a unified layer to access information in other databases and systems.

Whether you start with a single application that requires a graph database, a full knowledge model, or a unified semantic layer over your data lake, Stardog has a natural evolution trajectory such that every investment is entirely reusable.

When to Use Graph

Graph lends itself well to the complex data and datasets enterprises seek to connect and understand. Graph is used for everything from fraud detection to recommendation engines. A common growth scenario is for organizations to move from a single graph database use case to a knowledge graph and then to a big data semantic layer.

Common Graph Databases Use Cases

Common Graph Databases Use Cases

Discussed by industry

Graph Database Examples

Graph Database Examples

Customer stories

Important Graph Features

Given these use cases and examples, we‘ve focused our platform on developer enablement, enterprise performance, and ease of use for non-expert users. Here are some of the most important features a graph solution should include:

Enterprise performance

Enterprises need software that meets their scalability demands. Our trillion triple challenge showcases how Stardog can scale to meet whatever your graph need is, at a fraction of the compute cost.

Graph analytics and path queries

A primary benefit of connected data is to interrogate the path of one node to another to answer complex questions. Stardog is used for executing algorithms such as PageRank, shortest distance, and even detecting cycles in the graph.

Storing queries

Not only does Stardog provide the ability to compose queries for rapidly building new RESTful endpoints but you can also parameterize them for flexibility.

Standardized query

As participants in open standards bodies at the W3C, Stardog implements the W3C standards for data formats [RDF], query language [SPARQL], and schema models describing the shape of the graph [OWL].

SQL Support

The lingua franca of most legacy systems is still SQL. That’s why Stardog provides SQL support to enable your Tableau or PowerBI analysts to explore hops along the graph in a familiar table presentation.

Ease of use

We’ve built everything on standards but we’ve removed the complexity of knowing and working with the standards directly. You don’t need to know how to write a syntactically-correct query.

Machine learning and data science enablement

Stardog has a formal partnership with Databricks and supports working in languages like Python, R, and Java.

Learn More

Report
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Graph technology is now a key tool for uncovering relationships across diverse data. Based on the increasing usage, popularity, and maturity of graph, Gartner estimates that the market for graph technologies will grow to $3.2 billion by 2025.

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