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I generally agree with you. But, I think we also need to constantly evaluate our view about the current way we do things. For example, in 2012-2013 time frame, I was involved in a project dealing with an insane amount of data. I studied Hadoop in the spare time and applied what I learned at work. I felt grateful that it helped with the scale and performance tremendously. Fast forward to 2016, I was just starting to learn more about Spark (even though it was available for a while by then) to replace the slow execution model of Hadoop MapRed for our iterative model. Again, applying what I learned to the project helped us to cut the R & D time a lot. Plus, for most of the folks who start with Spark, they don't have the first hand experience of the original MR idea implemented in Hadoop. It is a pain and slow to use Hadoop MR model but nevertheless it makes you feel more appreciated with things like Spark/Flink.

To sum it up: you don't need to constantly chase after the greatest & latest framework. But, you need to constantly review new inventions/technologies to help you improve the way you currently do things. I am grateful for still having the passion to improve things around me!

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