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Hadoop >> mail # user >> real time analytics on hadoop using spark or storm


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Re: real time analytics on hadoop using spark or storm
spark streaming is a mini job, which can update every 150ms, but storm is
long live process.
 On 2013-12-07 9:12 AM, "Jay Vyas" <[EMAIL PROTECTED]> wrote:

> 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.
>
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> 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