spicyusername has it right. There's a concept called COST - Configuration that Outperforms a Single Thread, which basically asks the question: "how many cores/

spicyusername has it right. There's a concept called COST - Configuration that Outperforms a Single Thread, which basically asks the question: "how many cores/servers does a system need to outperform a single thread/core". Often this means that to outperform a single beefy server you need to get to 100+ servers to overcome the coordination costs and actually do more processing or process more faster. Lots of workload specific details here, but the general application in data engineering is "How many nodes does your spark cluster need to outperform my laptop, or how many nodes does your cluster need to outperform a single turbo.large.beef instance?" My belief is that most companies chase scalability wrong in data. That said, there are some companies that handle telemetry at scale and actually need to record and analyze billions of events per second. In these cases you will trivially hit the scaling limits of a single server. 99% of companies are not here. I could be convinced that this number is actually as low as 90% :)