It was a declarative statement designed to elicit further explanation. If someone is brand new and trying to figure out how to eat the elephant as it were, you kind of want to burn things down to their essentials. If MapReduce isn’t going to be part of the ecosystem in the future, one does not want to spend hours learning how to write MapReduce jobs.
B. From: Marco Shaw Sent: Tuesday, July 01, 2014 3:50 PM To: user Subject: Re: The future of MapReduce Sorry, not sure if that's a question. Hadoop v1=HDFS+MapReduce Hadoop v2=HDFS+YARN (+ MapReduce part of the core, but now considered optional to "get work done") v2 adds a better resourcing framework. Now you can run Storm, Spark, MapReduce, etc. on Hadoop and mix-and-match jobs/tasks with whatever your requirements, which may actually be both batch "stuff" and/or real-time. Not sure if that clarifies things... Just like you can evaluate all kinds of Apache ecosystems products to meet your needs, MapReduce is no longer the only kid on the bock. On Tue, Jul 1, 2014 at 3:07 PM, Adaryl "Bob" Wakefield, MBA <[email protected]> wrote: From your answer, it sounds like you need to be able to do both. From: Marco Shaw Sent: Tuesday, July 01, 2014 10:24 AM To: user 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.
