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Insights on Neptune and Graph Databases: Seeking Validation

Avatar of Beegee A.Beegee A.
·Nov 23, 2021 11:21 AM

Hello folks. I’m very new to graph databases and I’d like to share some recent insights that I hope you can validate

  • Neptune is closer to a transactional database than an OLAP

  • Because of the transactional nature, graph analytics operations like PageRank, Label Propogations, forms of clustering tend to not be done in Neptune + Gremlin

  • For analytical operations

, it is more common to export Neptune data to Spark (EMR/Glue) and perform said operations there. Curious if any of these assumptions are incorrect before I tattoo them on my left arm.

5 comments

· Sorted by Oldest
  • Avatar of François S.
    François S.
    ·

    I would say you are correct but would also check with Ora L.

  • Avatar of Ora L.
    Ora L.
    ·

    Neptune was indeed designed as an OLTP database, and performs best in transactional workloads where queries touch a limited “graph neighborhood”. That said, there are already many examples of companies using Neptune (say, with Gremlin) for some analytics tasks, albeit large-scale graph algorithms (such as PageRank) are not what the current version of Neptune is optimal for. A recent addition to Neptune’s capabilities is integration with AWS SageMaker and its ML capabilities (including the Deep Graph Library), and this may be applicable.

  • Avatar of Ora L.
    Ora L.
    ·

    https://aws.amazon.com/neptune/machine-learning/

  • Avatar of Beegee A.
    Beegee A.
    ·

    You guys are awesome. Thank you!

    :partyparrot:1
  • Avatar of Ora L.
    Ora L.
    ·

    😀

    :partyparrot:1