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Do you have examples of such features? Most production deployments of these kinds of things don't upgrade THAT often. You have a point that abstraction layers
by agibsonccc 10y ago
Do you have examples of such features? Most production deployments of these kinds of things don't upgrade THAT often.
You have a point that abstraction layers can limit you since it's another compatibility layer, but looking at what's done in actual practice I don't see it being that bad.
If anything, it should be evaluated on a case by case basis.
Flink is actually better tech overall for my use case, but a lot of customers want spark streaming since it's already installed. Having beam where we can do both is kinda nice.
- nl 10y agoWe had to abandon Cloudera (and Horton Works) Hadoop distributions because they didn't ship R support for Spark quickly enough (in 1.3 or 1.2 or whatever version that was). We jumped from 1.5 to 1.6 because of algorithms in MLLib (although that turned out to be a bit of a disappointment).
- agibsonccc 10y agoMost organizations I see tend to just wait...this sounds more like 1 off to me. You're at a unique employer if the data scientists have that much power..