Are people actually running heavy analytic workloads using Riak map-reduce?
This assumes a pedantic qualification of "big data" handling -- does storage count, as opposed to the actual processing of the data? But I think that's an important distinction in this context, as it strikes the heart of the dichotomy between concurrency and parallelism.
(Moreover, I've never really been sure how much Riak is being used for actual 'big data', as opposed to being a master-less, highly available repository for 'regular data' (distinction between 'big' and 'regular' deliberately left vague). More so since Riak is key-value as opposed to columnar, but I suppose that depends on your workload. But that's all besides the point here.)
This assumes a pedantic qualification of "big data" handling -- does storage count, as opposed to the actual processing of the data? But I think that's an important distinction in this context, as it strikes the heart of the dichotomy between concurrency and parallelism.
(Moreover, I've never really been sure how much Riak is being used for actual 'big data', as opposed to being a master-less, highly available repository for 'regular data' (distinction between 'big' and 'regular' deliberately left vague). More so since Riak is key-value as opposed to columnar, but I suppose that depends on your workload. But that's all besides the point here.)