A graph database is a complex system represented as a network of nodes and connections called a labeled property graph. The nodes represent entities such as people, products, or orders, while the relationships between them are directional and know their beginning and ending node. A native graph database platform stores and accesses data in its native property graph form to maximize efficiency and performance. Non-native approaches, however, can be flawed with performance, integrity, ease-of-use, and scalability risks due to added processing layers that translate graph queries into relational-table-based storage and processing models used by underlying database technologies. To be a native graph DBMS, a technology must conform to 15 rules, including native storage and modeling, first-class relationships, real-time availability, index-free adjacency, comprehensive data management, discrete management, Cypher support, nonsubversion, ACID transactions, consistent reads, consistent writes, integrity independence, data independence, seamless presentation, and query performance optimization. Despite the rise of graph databases, relational technology is not dead, but rather, it's more suitable for tabular data with static schemas, while graph databases excel in handling highly connected or changing datasets with sub-second response times.