Different database engines are built to be good at a specific set of operations. 

Relational engines, for example, are typically optimised for transaction control and protecting data from damage and loss during update.
 
They are typically not optimised for detecting fraud and performing recommendations (“Customers who bought this book frequently bought….”).  Graph databases are essentially the opposite, poor at transactions and good at tasks such as fraud detection and recommendations.  The key to using graph
databases effectively is understanding not only how they work but why they were designed that way – in other words, understanding what underpins their strengths and weaknesses.  So this talk will explore their origins and how and why they work.

 
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