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Lately, we’ve seen a new wave of interest in distributing in-process, in-memory systems – projects like Deepseek’s Smallpond (distributed DuckDB) and DataFusion for Ray are getting a lot of buzz. But let’s be honest: this isn’t a new trend. For decades, “going distributed” (i.e. horizontal scale-out via partitioning) has been the go-to move when things get big, or might get big someday. But here’s the thing: just because something can be distributed doesn’t mean it should be. In this talk, we challenge the idea that “distributed” is the right default. We’ll unpack what really happens when you scale out and show how those tradeoffs can crush performance and developer sanity if you’re not careful. Instead, we’ll explore how to make big problems small and only layer on distributed strategies when it’s clearly the right solution. This isn’t a talk against distributed systems – it’s a talk about earning them. You’ll walk away with a systems-thinking mindset that helps you scale with purpose, not panic. Because sometimes, the smartest way to go big… is to stay small… until you absolutely can’t. https://x.com/stephwangbuilds https://github.com/stephaniewang526 Talk from Systems Distributed '25: https://systemsdistributed.com Join the chat at https://slack.tigerbeetle.com/invite