Powered by a native graph database, Neo4j stores and manages data in its more natural, connected state, maintaining data relationships that deliver lightning-fast queries, deeper context for analytics, and an easily modifiable data model.
Analysts and data scientists can incorporate network structures to infer meaning, increase ML accuracy, and drive contextual AI – making better predictions with the data they already have.
Performance
For intensive data relationship handling, graph databases improve performance by several orders of magnitude. With traditional databases, relationship queries will come to a grinding halt as the number and depth of relationships increase. In contrast, graph database performance stays constant even as your data grows year over year.
Flexibility
With graph databases, IT and data architect teams move at the speed of business because the structure and schema of a graph model flexes as applications and industries change. Rather than exhaustively modeling a domain ahead of time, data teams can add to the existing graph structure without endangering current functionality.
Agility
Developing with graph databases aligns perfectly with today’s agile, test-driven development practices, allowing your graph database to evolve in step with the rest of the application and any changing business requirements. Modern graph databases are equipped for frictionless development and graceful systems maintenance.
Today’s enterprise organizations use graph database technology in a diversity of ways:
Neo4j Graph Data Science is a software platform helping data scientists uncover the connections in big data to answer business critical questions and improve predictions.
Businesses use graph data science insights to pinpoint interactions that indicate fraud, identify similar entities or individuals, improve customer satisfaction through better recommendations, and optimize supply chains.
Neo4j Graph Data Science makes it easier to unlock answers because it puts relationships first, instead of keeping them hidden within rows and columns. Data scientists can analyze these relationships in a flexible workspace using a library of over 65+ pre-tuned algorithms, connected data techniques, and in-graph machine learning (ML) models. Its scalable infrastructure works with existing data science tools and workflows, so you can easily move from proof of concept to production.
The dedicated workspace integrates ingestion, analysis, and management to easily improve models without rebuilding their workflow. ML Ops allows data scientists to focus on extracting insights, training ML models, and deploying projects across production areas.
Cypher is a powerful, intuitive, graph-optimized query language that understands, and takes advantage of, data connections. It’s user-friendly, easy to learn, and follows connections – in any direction – to reveal previously unknown relationships and clusters.
When trying to find patterns or insights within data, Cypher queries are much simpler and easier to write than massive SQL joins. Since Neo4j doesn’t have tables, there are no joins to worry about.
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