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Siliconangle
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The new data stack: Unifying OLTP, lakes and graphs for scalable AI
- Powering AI at scale requires an infrastructure that can rapidly unlock the value of data, with data lakes and graph databases becoming valuable tools for analytic workloads.
- Nikita Shamgunov, Tanya Bragin, and Philip Rathle discuss analytic workloads and connectivity as essential components in the evolving infrastructure.
- Shamgunov highlights the three separate worlds of data lakes, OLTP, and applications and emphasizes the need for connectivity to drive business and AI.
- The conversation touches on the decreased cost of building applications, enabled by platforms endorsing high velocity development and a unification layer for connectivity.
- Rathle champions graph databases like Neo4j for providing essential connectivity in application deployment, citing a use case with Klarna Group PLC.
- Bragin notes the evolution towards a unified architecture for data lakes, supporting observability, business intelligence, and the unification of historically separate stacks.
- The development of a single unified architecture for data lakes is discussed, emphasizing the move towards open data lakes as a unifying layer for analytics.
- The discussion also emphasizes the importance of database market players in providing the connective tissue and unification layer essential for application deployment.
- The article concludes with a message from co-founder John Furrier, promoting engagement with SiliconANGLE's content and community.
- SiliconANGLE Media operates at the forefront of digital media innovation, serving tech professionals with influential content and strategic insights.
- The article provides valuable insights into the evolving landscape of infrastructures supporting AI at scale, with a focus on connectivity and unification layers.
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