Modern Data Breaks Relational Models
Since the 1980s, the relational database has been the preferred platform for enterprise software applications that process highly structured data. But the demands of modern business trigger seismic shifts that push relational models to their breaking point. The reasons are many and include:
- New online services, applications and data sources are emerging constantly
- Businesses are integrating external data sources over which they have no control
- Data structures are changing as applications evolve rapidly
- Data sources are growing in size and complexity at meteoric rates
- The relationships between data elements—which are often more valuable than the data itself—are growing at even faster rates
All these new developments point to a fundamental problem. Today’s data is increasingly complex, changing and highly connected—and relational technologies just aren’t built for those demands. Modern database platforms must be able to handle the dynamic connections between elements in today’s datasets with more agility and performance than relational can deliver.
The Relational Reality Distortion
The term relational can be deceiving. It derives from the ability to relate two row-and-column tables of data to each other—but rows and columns are not how data exists in the real world. Instead, data exists as objects and the relationships between those objects.
The bottom line is that relational databases can’t capture or mine the real meaning of the rich relationships that drive opportunities, decisions and hidden answers in the modern world.
Graph Databases Are Built on Relationships
Today’s information managers are turning from relational to graph databases like Neo4j to make sense of the complex, rapidly changing, relationships in modern, connected-data applications.
The ability to create simple, efficient connections makes graph technology an ideal foundation for applications that manage social and operational networks, make recommendations, discover opportunities, expose hidden relationships, and drive new revenues.
But the benefits don’t stop there. Compared to old relational systems, Neo4j graph applications are easier to design, faster to develop, simpler to maintain, and deliver dramatic savings.
Dramatically Faster Development Cycles
Neo4j’s native graph approach dramatically reduces development times from the elongated cycles required for relational applications. Its faster path to the finish line derives from its ability to:
- Eliminate data schema design and implementation tasks
- Simplify the creation and fine-tuning of queries
- Minimize the need for functionality discussions and sync meetings between users and developers
In addition, for complex data queries, transformations and transactions, Neo4j also includes Cypher, its industry-standard graph query language. A few lines of Cypher code can perform the work of hundreds of lines of relational SQL queries.
Simpler, More Efficient Application Maintenance
Even after initial application deployment, Neo4j continues to speed and simplify development as its schema-free approach adapts easily to new data availability, functionality requests, market growth, product releases, competitive responses and organizational changes. Put simply, the more often underlying data and its connections change, the higher the payoff from using graph database technology.
Dramatic Savings Over Relational Approaches
Over the entire development cycle of applications with connected data, Neo4j’s native graph approach streamlines communications, speeds development and simplifies application maintenance—dramatically reducing the time and effort required by relational database approaches.