Jitender Aswani was customer zero for Presto at Meta, where a billion daily active users generated queries that took hours to return. He watched that drop to minutes, scaled the same technology at Netflix across 300 million subscribers, and now runs engineering and security at Starburst, the $3.35 billion platform built on Trino.

His argument: every enterprise AI project that stalls is fighting the same hidden battle. The agents can query the model fine. They just can't reach the data. The average enterprise runs 52 to 200 data sources, and a decade of moving all of it into one lake produced ETL debt, governance problems, and pipelines that break whenever a SaaS vendor adds a column.

Federation is the only model that scales with entropy.

We cover:

  • Why Presto changed what Meta could experiment on, and how that compounded product velocity
  • What broke when Jitender took the same technology to enterprises running 52 to 200 data sources
  • Why centralization stopped working once data grew faster than the ability to move it
  • What happened to Starburst's query volume the day they shipped an MCP server
  • The FinOps agent that fired queries for 30 minutes against data it never had
  • How AIDA turns ad hoc analysis into workflows using skills and MCP servers
  • Why a context graph is different from a knowledge graph, and why ontology decides agent accuracy

(0:00) Enterprises run on 52 to 200 data sources
(0:25) Intro
(2:18) Customer zero for Presto at Meta
(9:50) Scaling to trillions of events at Netflix
(15:11) Taking Trino from Silicon Valley to 10,000 enterprises
(20:24) The 2011 research that predicted conversational analytics
(28:54) Why centralization can't scale with entropy
(32:26) The agent query explosion and what MCP did to volume
(41:44) Inside AIDA, Starburst's conversational analytics product
(46:56) Context graphs versus knowledge graphs
(51:17) Where to follow Jitender's work

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More episodes: https://chainofthought.show

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