There’s a narrative forming in the data world that if you have enough data, the lakehouse is the obvious place to put it. Open formats, cheap storage, shared access across tools. These are real advantages, and for the right workloads, the model works extremely well.
But that narrative tends to blur an important distinction. Storage has largely been solved, yet what actually determines whether a platform works in practice is how it behaves when the data is used—when it’s being queried, updated, and expected to respond under load. That’s where the gap between storage and execution becomes clear.
Lakehouses become much more powerful when paired with an execution layer designed for low-latency, high-concurrency workloads.
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singlestore.com/blog/the-lak…