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AI is Changing Data Engineering Fast!

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Most Data Teams Are Doing It Wrong | Podcast

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How to Actually Break Free From Notebook Engineering

At some point, every data engineer has to confront a slightly uncomfortable truth about how they work: the tools they use are not just tools but habits, and those habits quietly shape how they think, how they build, and ultimately what kind of systems they produce. That realization tends to hit hardest when someone points […]

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Why I Finally Pulled the Plug on Polars and Moved to DuckDB

I finally hit that point that every engineer eventually reaches with a tool they once loved, that moment where frustration quietly builds over time and then suddenly flips into a decision, not because of one catastrophic failure but because of the accumulation of too many small ones. That was me with Polars. After years of […]

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Databricks Metric Views and the Reality of the Semantic Layer

I’ve written before about the elusive “Semantic Layer,” that mythical construct every data team eventually talks about building. It’s the idea of pulling all business logic, calculations, and definitions into a single place so everyone agrees on what the numbers actually mean. Anyone who has worked in data long enough knows the pain this is […]

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Agent Bricks and the Commoditization of AI Systems

I recently spent some time poking around Agent Bricks from Databricks, and it’s a pretty good representation of where we are in the AI cycle right now. Whether you’re skeptical or all-in, it’s hard to ignore the fact that agent-based systems are no longer theoretical. They’re here, and they’re being used to automate real workflows. […]

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