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Graph Database—When Relationships Matter!

Are exponential data growth and complex joins choking your application performance?

If you are dealing with highly connected data objects, consider replacing or supplementing your RDBMS with a graph database. Graphs may offer a more natural way to model your domain, solving performance and scalability challenges.

Relationship—the First-Class Citizen

Relationships are first-class citizens in a graph database. By storing data as a set of explicit relationships connecting nodes in a graph-like structure, graph databases allow data to be modeled naturally, closer to the problem domain. Path traversals make querying easy, giving graph databases their celebrated low-latency.

Key Features

  • Human-friendly data visualization
  • Data stored as graph structures
  • Flexible schema
  • High query performance on complex data structures
  • Quick access to associative datasets
  • Horizontal scaling
  • Parallel querying by multiple users
  • Inexpensive query operations through index-free adjacency

The Neo4j Advantage

Neo4j, the world's first and leading graph database, is also the most mature and enterprise-ready graph database deployed worldwide.

  • Scalable to billions of nodes and relationships
  • Full support for ACID transactions
  • Powerful query language—Cypher
  • High availability
  • Built-in algorithm to find the shortest path

Common Use Cases

Master Data Management

Graph databases are ideal for storing and exploring the hierarchies and complex relationships within the master data. They outshine traditional data management solutions with their modeling flexibility and capability for rich data visualization and analytics.

Recommendation Systems

Graph data models are used to identify customer preferences and provide recommendations based on those preferences.

Fraud Detection

From flagging unusual events to uncovering hidden patterns of activity, graph databases provide real-time support to the insurance and banking sector.


Genome analysis, the next frontier in bioinformatics, requires high throughput sequencing and analysis of annotated graphs, a requirement graph database efficiently fulfills.

IT Asset Management

Graph databases support an array of asset management needs—mapping inventory, managing assets, ensuring timely upgrades, and delivering high-network performance.

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