Someone asked me this exact question last week, and it’s a good one because both setups look the same if you squint. A bunch of machines, some shared storage in the middle, work spread across nodes. So why does one get called “big data” and the other “microservices”? Are they just two words for the same cluster? Honestly, no. They’re built on opposite assumptions about one thing: where the data lives and who moves to whom.
The “just scale microservices” question keeps coming up whenever Spark enters the conversation. It sounds logical — you already have distributed services, just throw more at the problem. But this comparison collapses under a pretty basic question: what kind of problem are you actually solving?
It Is Not a Database. Not a Queue. People come to Spark expecting something like a faster database or a smarter Kafka. Neither is accurate.