Trends in Data Management: From Source to BI and Generative AI

Sudhir Hasbe on vector search, analytics, lakehouses, knowledge graphs, and more.

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Sudhir Hasbe, Chief Product Officer at Neo4j, and a longtime technical and product leader in the data management space.  We discussed several important trends including:

  • Vector Search and Vector Databases
  • Analytics (including popular open source projects like DuckDB, Clickhouse, and Postgres)
  • Lakehouses
  • Knowledge Graphs
  • Multimodal systems and HTAP

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    ❛We’re all in data space, and we’re all capable of building the same thing. Just look at the last six months. How many existing systems were able to add vector support to their databases? Almost every major database provider has already done it. What I believe is that it’s much easier for the core database companies to add vector indexes and vector searches within themselves than it is for a dedicated vector store to integrate all the capabilities that different systems offer. In eternity, they could probably achieve anything. I’m pretty confident that databases like Postgres, and many others, can incorporate all the necessary capabilities. So, I don’t see that as a challenge. For instance, we were able to add vector support to our database in less than three months and then rolled it out to all our customers. And we’ll only improve from here, blending capabilities between knowledge graphs and vector searches, both explicit and implicit relationships. We can merge these features for various use cases. So, what I’m getting at is that it’s easier for existing database companies to add vectors than for vector databases to integrate all the functionalities of a relational database or a NoSQL system like Mongo, or search systems like Elastic. All of those are commendable in their own right.❜
    Sudhir Hasbe, Chief Product Officer at Neo4j.

Interview highlights – key sections from the video version:


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