A fundamental objective of data science & AI is, of course, to discover insights. What can the data reveal? Where are the opportunities? What kind of predictions can be made? Are there any looming threats that can be inferred? While RDBMS (relational databases) are ideal for some situations, graph databases are far superior for many others.
The world we live in is just as much about the relationship between entities (data) as the entities themselves. Relational databases are actually a misnomer because they don’t convey the relationships between data points well. Graph databases do. Because of that, areas like fraud detection, real-time recommender systems, network/IT operations monitoring and several others are much better suited to graph DBs.
THE RISE OF GRAPH.
It’s no surprise that as data science and artificial intelligence have graduated from emerging disciplines into technological juggernauts ever rapidly impacting most sectors, that advancements in databases themselves have played a key role in making such strides possible. Innovation in the field has exploded.
While the evidence is everywhere that graph database technology is red hot, the accompanying chart demonstrates that over the last handful of years no other type of databases have been rising in popularity nearly as quickly.