Traditional relational databases, the powerhouse of software applications since the 1980s, work well when your data is predictable and fits well into tables, columns, rows, and wherever queries are not very join-intensive.
But there are rich, connected domains all around us that relational software isn’t so well equipped at dealing with.
Relational database-management systems (RDBMS) only model data as a set of tables and columns, carrying out complex joins and self-joins when the dataset becomes more inter-related. Such queries are technically complex to construct and expensive to run. Plus, making them work in real time while end users wait is not easy, with performance faltering as the total dataset size increases.
Hence the rise of another type of database, optimized for connected data: the graph database. Graph databases are compelling because they enable companies to make sense of the masses of connected data that exist today.
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