Thanks Matei.

On Tue, Jul 15, 2014 at 11:47 PM, Matei Zaharia <matei.zaha...@gmail.com>
wrote:

> Yup, as mentioned in the FAQ, we are aware of multiple deployments running
> jobs on over 1000 nodes. Some of our proof of concepts involved people
> running a 2000-node job on EC2.
>
> I wouldn't confuse buzz with FUD :).
>
> Matei
>
> On Jul 15, 2014, at 9:17 PM, Sonal Goyal <sonalgoy...@gmail.com> wrote:
>
> Hi Rohit,
>
> I think the 3rd question on the FAQ may help you.
>
> https://spark.apache.org/faq.html
>
> Some other links that talk about building bigger clusters and processing
> more data:
>
>
> http://spark-summit.org/wp-content/uploads/2014/07/Building-1000-node-Spark-Cluster-on-EMR.pdf
>
> http://apache-spark-user-list.1001560.n3.nabble.com/Largest-Spark-Cluster-td3782.html
>
>
>
> Best Regards,
> Sonal
> Nube Technologies <http://www.nubetech.co/>
>
>  <http://in.linkedin.com/in/sonalgoyal>
>
>
>
>
> On Wed, Jul 16, 2014 at 9:17 AM, Rohit Pujari <rpuj...@hortonworks.com>
> wrote:
>
>> Hello Folks:
>>
>> There is lot of buzz in the hadoop community around Spark's inability to
>> scale beyond the 1 TB datasets ( or 10-20 nodes). It is being regarded as
>> great tech for cpu intensive workloads on smaller data( less that TB) but
>> fails to scale and perform effectively on larger datasets. How true it is?
>>
>> Are there any customers in who are running petabyte scale workloads on
>> spark in production? Are there any benchmarks performed by databricks or
>> other companies to clear this perception?
>>
>>  I'm a big fan of spark. Knowing spark is in its early stages, I'd like
>> to better understand boundaries of the tech and recommend right solution
>> for right problem.
>>
>> Thanks,
>> Rohit Pujari
>> Solutions Engineer, Hortonworks
>> rpuj...@hortonworks.com
>> 716-430-6899
>>
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>
>
>
>


-- 
Rohit Pujari
Solutions Engineer, Hortonworks
rpuj...@hortonworks.com
716-430-6899

-- 
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