There is excitement and innovation in the world of graphs. As a result, several vendors are releasing graph databases or adding graph capabilities to their existing database offerings. This does not mean that all graph databases are created equal.
The design of a graph database has a significant influence on its performance and dependability. Graph databases fall either into the native or non-native category. Native graph databases are those designed to handle graph workloads across the entire stack whereas non-native databases piggyback atop another DBMS.
According to Dr. Jim Webber, Chief Scientist for Neo4j. Native graph databases process graph queries faster, scale better and run efficiently with less hardware. The databases retain their hallmark query speed as the data set grows and continues to prove its efficiency even when there have been 10 x reductions in the hardware. One reason native graph databases outperform is their choice of storage. In addition, native graph databases are designed to use file systems to process graphs and handle graph workloads efficiently.
Click here to continue reading how native graph databases are critical for real-world systems and the challenges associated with leveraging non-native graph databases.
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