The Fusion of Vector Search and Graph Data: Neptune Analytics by AWS

There’s been a debate of sorts in AI circles about which database is more important in finding truthful information in generative AI applications: graph or vector databases. AWS decided to leave the debate to others by combining the best of both capabilities in a new service announced today at AWS re:Invent called Neptune Analytics. Swami Sivasubramanian, vice president of data and machine learning at AWS, announced the new tool on stage at the the AI keynote at re:Invent, saying that the cloud giant wanted to create something that combines the best of both approaches.
“Since both graph analytics and vectors are all about uncovering the hidden relationships across our data, we thought to ourselves: ‘what if we combined vector search with the ability to analyze massive amounts of graph data in just seconds,’ and today, we are doing just that,” he said.
He explained that the new service helps customers analyze existing Neptune graph data or data lakes on top of S3 storage, taking advantage of vector search to find key insights. “Neptune analytics makes it easier for you to discover relationships in your graph with vector search by storing your graph and vector data together,” he said.

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