Inside Google’s Database Infrastructure for the AI Era
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Inside Google’s Database Infrastructure for the AI Era

B2Upper-intermediateSeptember 15, 20261 hr 18 min
  • Interview
  • Technology

Historically, databases were responsible for storing data and returning exact results in response to queries. However, AI is now bending that contract in a new direction. Applications increasingly expect structured and unstructured data to come together. This is pushing databases into territory that looks more like search, where relevance and ranking matter and results are no longer strictly exact. Agents are also beginning to write their own queries and even propose their own schemas, which raises new questions about how data should be structured, governed, and trusted. Sailesh Krishnamurthy is a VP of Engineering at Google, and...

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Historically, databases were responsible for storing data and returning exact results in response to queries. However, AI is now bending that contract in a new direction. Applications increasingly expect structured and unstructured data to come together. This is pushing databases into territory that looks more like search, where relevance and ranking matter and results are no longer strictly exact.

0:26

Agents are also beginning to write their own queries and even propose their own schemas, which raises new questions about how data should be structured, governed, and trusted. Silesh Krishnamurti is a VP of Engineering at Google, and in this episode, he joins Matt Merrill to discuss his background, how databases have evolved over the past 50 years, and where

0:49

the field is heading as AI reshapes how data is queried, structured, and trusted. Matt Merrill is a software engineering leader with over 20 years of experience building and scaling software teams across enterprise and product-focused organizations. His background is in backend development, cloud architecture, and distributed systems design. He currently

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