Analytics

Many scalable data store technologies are not particularly suitable for business analysis or online analytical processing (OLAP) uses. When working with large amounts of data, coalescing desired data can be tricky with relational database management systems (RDBMS). Some enterprises will even duplicate their RDBMS into a separate system for OLAP so as not to interfere with their online transaction processing (OLTP) workloads.

Neo4j can scale to present meaningful data about relationships between different enterprise-marketing entities. In his graduate thesis titled GraphAware: Towards Online Analytical Processing in Graph Databases, researcher Michal Bachman illustrates this difference in a simple comparison of traversing relationships in both RDBMS and graph database management systems (GDBMS). Bachman observes that What might be a straightforward shallow breadth-first search in a GDBMS (hence considered OLTP) could be a very expensive multi-join operation in RDBMS (thus qualifying as OLAP).^[2]

However, Bachman also urges caution with analytical workloads on graph databases, stating that graph databases lack native OLAP support.^[2] This implies that additional tools may need to be built to suit specific business analysis needs.