the rise of graph databases finding the right representation for your data since the early the most common type of database one had to rely on was relational one laterothers emergedsuch as the hierarchical database (for exampleims)and the graph database' closest relativethe network database (for exampleidmsbut during the last decades the landscape has become much more diversegiving end-users more choice depending on their specific needs considering the recent development of the data that' becoming availabletwo characteristics are well suited to be highlighted here the first one is the size of the data and the other the complexity of the dataas shown in figure key-value data store column-value data store document databases size of data graph databases relational databases complexity of data figure this figure illustrates the positioning of graph databases on two dimensional space where one dimension represents the size of the data one is dealing withand the other dimension represents the complexity in terms of how connected the data is when relational databases can no longer cope with the complexity of data set because of its connectednessbut not its sizegraph databases may be your best option as figure indicateswe'll need to rely on graph database when the data is complex but still small though "smallis relative thing herewe're still talking hundreds of millions of nodes handling complexity is the main asset of graph database and the ultimate "whyyou' use it to explain what kind of complexity is meant herefirst think about how traditional relational database works contrary to what the name of relational databases indicatesnot much is relational about them except that the foreign keys and primary keys are what relate tables in contrastrelationships in graph databases are first-class citizens through this aspectthey lend themselves well to modeling and querying connected data