Data Federation, Not Centralization, Is What Enterprise AI Needs episode artwork

EPISODE · Jul 16, 2026 · 52 MIN

Data Federation, Not Centralization, Is What Enterprise AI Needs

from Chain of Thought | AI Agents, Infrastructure & Engineering · host Conor Bronsdon

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 velocityWhat broke when Jitender took the same technology to enterprises running 52 to 200 data sourcesWhy centralization stopped working once data grew faster than the ability to move itWhat happened to Starburst's query volume the day they shipped an MCP serverThe FinOps agent that fired queries for 30 minutes against data it never hadHow AIDA turns ad hoc analysis into workflows using skills and MCP serversWhy 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 workConnect with Jitender Aswani:LinkedIn: https://www.linkedin.com/in/jitenderaswani/Starburst: https://www.starburst.io/Connect with Chain of Thought host Conor Bronsdon:Newsletter: https://newsletter.chainofthought.show/Twitter/X: https://x.com/ConorBronsdonLinkedIn: https://www.linkedin.com/in/conorbronsdon/YouTube: https://www.youtube.com/@ConorBronsdonMore episodes: https://chainofthought.show

Episode metadata supplied by the publisher feed · Published Jul 16, 2026

Embed this episode

Jitender Aswani was customer zero for Presto at Meta, scaled it at Netflix, and now runs engineering and security at Starburst, the $3.35 billion data platform built on Trino. He explains why the average enterprise has 52 to 200 data sources, why centralizing them will never work, and what happened to Starburst's query volume the day they shipped an MCP server.

Distinct summary based on available episode metadata or transcript content.

NOW PLAYING

Data Federation, Not Centralization, Is What Enterprise AI Needs

0:00 52:42

No transcript for this episode yet

We transcribe on demand. Request one and we'll notify you when it's ready — usually under 10 minutes.

No similar episodes found.

No similar podcasts found.

Frequently Asked Questions

How long is this episode of Chain of Thought | AI Agents, Infrastructure & Engineering?

This episode is 52 minutes long.

When was this Chain of Thought | AI Agents, Infrastructure & Engineering episode published?

This episode was published on July 16, 2026.

Can I download this Chain of Thought | AI Agents, Infrastructure & Engineering episode?

Yes. Use the download control on the episode player to save the publisher-provided media file.
URL copied to clipboard!