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Special Edition on Databases

Special Edition on Databases

It worked in theory. Let’s talk about production.

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Arpit Bhayani

Arpit Bhayani

Databases Were Not Designed For This

Description Databases were not designed for agents. They were built around a set of implicit assumptions: callers issue predictable queries, connections are short-lived, bad queries fail loudly, and schemas are a contract with engineers. Agentic systems break every one of these assumptions. Agents reason their way to queries, hold connections while an LLM thinks, retry operations unpredictably, a… more
  • 1 comment
  • Confirmed
  • 01 Apr 2026
Session type - select the format for your session: 30-minute talk – technical deep dive

Varuni

Rethinking Data Systems for the Age of LLMs

Introduction Over the past few years, the center of gravity in data systems has begun to shift. While traditional database workloads were dominated by deterministic transactions and analytical queries, both industry and academic evidence now point to a rapid rise in AI-driven, token-based workload. Analysts estimate that 80% of enterprise data is unstructured, yet historically underutilized. Toda… more
  • 0 comments
  • Confirmed
  • 16 Apr 2026
Session type - select the format for your session: Birds of Feather (BOF) proposals – discussion on focussed topics

sarthak makhija

Fast on Paper, Slow in Reality: What We Got Wrong About Performance

Description In distributed systems engineering, a design that is “correct on paper” is only the beginning; the real challenge is making it “fast in reality.” This session offers a transparent post-mortem of the architectural assumptions we made while building a distributed key-value store from scratch in Go, and why several of those assumptions collapsed under production-grade pressure. We’ll mov… more
  • 1 comment
  • Confirmed
  • 25 Apr 2026
Session type - select the format for your session: 30-minute talk – technical deep dive

Mihai Budiu Presenter

Incremental Computation

Incremental computations repeatedly evaluate a function on some input values that are “changing”. The goal of an efficient implementation is to “reuse” previously computed results: when presented with a new change to the input, an incremental computation should only perform work proportional to the size of the changes of the input, rather than to the size of the entire dataset. more
  • 1 comment
  • Confirmed
  • 29 Apr 2026
Session type - select the format for your session: 30-minute talk – technical deep dive
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