Memory. You can load a huge graph into RAM now where you couldn’t 7 years ago. So it’s fast to work with a big graph now where you used to have to wait a long time for results.
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Yes abundant memory, but also the various algorithmic approaches are better understood and there are more implementations: succinct data structures, worse-case optimal joins, matrices / GraphBLAS etc. Schema-on-read makes bitemporality much simpler too http://opencrux.com ;)
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SaaS multiples on graph database based companies.
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I like how you can add instances incrementally, and you can call up and see the implied schema from these instances as it changes. I.e. builds coverage and logic just from tables of triples
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Stems from GAFAM and the tier below talking Knowledge Graphs to feed the AI goblins in search and reco.
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I’ve been using dgraph for the anti-abuse thing ive been building and it’s just...agile-pleasant
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GraphQL became popular?
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