Spark https://spark.apache.org/ is also getting a lot attention with its in-memory computations and caching features. Performance wise it is being touted better than mahout because machine learning involves iterative computations and Spark could cache these computations in-memory for faster processing.
On Tue, Jul 1, 2014 at 11:07 AM, Adaryl "Bob" Wakefield, MBA < [email protected]> wrote: > From your answer, it sounds like you need to be able to do both. > > *From:* Marco Shaw <[email protected]> > *Sent:* Tuesday, July 01, 2014 10:24 AM > *To:* user <[email protected]> > *Subject:* Re: The future of MapReduce > > It depends... It seems most are evolving from needing "lots of data > crunched", to "lots of data crunched right now". Most are looking for > *real-time* fraud detection or recommendations, for example, which > MapReduce is not ideal for. > > Marco > > > On Tue, Jul 1, 2014 at 12:00 PM, Adaryl "Bob" Wakefield, MBA < > [email protected]> wrote: > >> “The Mahout community decided to move its codebase onto modern data >> processing systems that offer a richer programming model and more efficient >> execution than Hadoop MapReduce.” >> >> Does this mean that learning MapReduce is a waste of time? Is Storm the >> future or are both technologies necessary? >> >> B. >> > >
