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MapReduce >> mail # user >> Re: real time analytics on hadoop using spark or storm


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Re: real time analytics on hadoop using spark or storm
Spark increases performance by using distributed shared memory.

Storm on the other hand gives you realtime performance by processing data
sets in small batches.

The case for Spark is when you want a more sophisticated data processing.

The case for Storm is when you have large volumes of incoming data and you
want to run a process every 1000 records.

If you want a better comparison, try comparing spark-streaming with storm.

On Fri, Dec 6, 2013 at 7:04 PM, Smarty Juice <[EMAIL PROTECTED]> wrote:

> can anyone explain what is the clear difference between spark and storm
>
> what are the use case of storm and spark?
>
> can it be used without haddop?
>
> what are the pros and cons of running with or without hadoop?
>
> thanks
>
>
--
Jay Vyas
http://jayunit100.blogspot.com
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB