Our current big data landscape prompts us to develop new analytical skills in order to make the best use of the abundance of datasets at hand. Traditionally, SQL databases such as PostgreSQL have been the databases of choice, and newer graph databases such as Neo4j have been relegated to the analysis of social network and transportation datasets. In this paper, we conduct a side by side comparison of PostgreSQL (using SQL) and Neo4j (using Cypher) using the MIMIC-III patient database as a case study. We found that, while Neo4j is more time intensive to implement, its queries are less complex and have a faster runtime than comparable queries performed in PostgreSQL. This leads to the conclusion that while PostgreSQL is adequate as a database, Neo4j should be considered a viable contender for health data storage and analysis.