The New Normal: What I Learned (or Un-Learned) at GraphConnect 2022
Blog post from Neo4j
Normalization in traditional relational databases serves a specific purpose, primarily designed to protect data integrity by reducing redundancy. However, graph computing models this concept differently, focusing on relationships as the fundamental structure of the database. Graph databases like Neo4j achieve efficiency without sacrificing data integrity through their inherent design, which doesn't require denormalization or reassembly of data for relationships. This approach enables query efficiency and parallel execution across graphs, allowing for federated queries, horizontal scaling, and hybrid cloud queries. The traditional relational model's emphasis on normalization is seen as less practical at scale, where graph models offer a more natural fit for complex, interconnected data. By building databases with graph structures from the outset, organizations can avoid unnecessary denormalization and re-normalization efforts, streamlining their data management processes.
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