Numerous graph algorithms have been developed to address a variety of problems in the industry, ranging from fraud detection to scheduling or even recommendation systems. Graph-processing frameworks are hence created to simplify the implementation of graph-based solutions. Nonetheless, the number of such frameworks has grown significantly over the past decades with varying benefits and drawbacks. Understanding the requirements and characteristics of each framework plays a vital role in the selection of a suitable solution to a given problem. In this work, we evaluate the performance and usability of 2 popular graph-processing frameworks Neo4j and Apache Spark GraphX by implementing a PageRank solution to solve a practical business problem derived from the Yelp dataset.