Your Enterprise is Driven by Connections
Your Data Should Do the Same
Today's world is no longer driven by data – it's driven by the connections between them.
Big data alone used to be enough, but enterprise leaders need more than just volumes of information to make bottom-line decisions. You need real-time insights into how data is related.
Data relationships drive today's intelligent applications, and you need a database that harnesses those connections for sustainable competitive advantage. That solution is a graph database.
Graphs – i.e., networks – are the most efficient and intuitive way of working with data, mimicking the interconnectedness of ideas in the human mind. Neo4j is built from the ground up to harness the power of graphs for real-time, bottom-line insights.
Discover the competitive advantage of using a graph database
What Is Neo4j?
Neo4j is a highly scalable native graph database that leverages data relationships as first-class entities, helping enterprises build intelligent applications to meet today’s evolving data challenges.
The Native Graph Database Difference
Neo4j: Built for Graphs from the Ground Up
Neo4j is a native graph database, designed to store and process graphs from bottom to top. On the other hand, non-native solutions only add a shallow graph processing layer to an RDBMS or other NoSQL data stores, resulting in sub-optimal performance.
Native Graph Storage
Neo4j uses native graph storage that is specifically designed to store and manage interconnected data. Each piece of data in Neo4j has an explicit connection to every related entity, meaning database queries can ignore anything that's not connected rather than crawl the entire dataset. The result is unparalleled speed and scale.
Non-native graph databases use relational or object-oriented databases for data storage instead, which becomes much more latent as data volume and query complexity grow.
Native Graph Processing
Neo4j's native graph processing (also known as “index-free adjacency”) is the most efficient means of processing graph data because connected nodes physically “point” to each other in the database. This means Neo4j can evaluate results at a rate of millions of hops per second delivering constant-time performance regardless of the size of the dataset.
Non-native graph processing often defaults to expensive index lookups which result in reduced performance.