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Posted to user@spark.apache.org by John Omernik <jo...@omernik.com> on 2016/02/09 20:36:27 UTC
Appropriate Apache Users List Uses
All, I received this today, is this appropriate list use? Note: This was
unsolicited.
Thanks
John
From: Pierce Lamb <pl...@snappydata.io>
11:57 AM (1 hour ago)
to me
Hi John,
I saw you on the Spark Mailing List and noticed you worked for ***** and
wanted to reach out. My company, SnappyData, just launched an open source
OLTP + OLAP Database built on Spark. Our lead investor is Pivotal, whose
largest owner is EMC which makes ***** like a father figure :)
SnappyData’s goal is two fold: Operationalize Spark and deliver truly
interactive queries. To do this, we first integrated Spark with an
in-memory database with a pedigree of production customer deployments:
GemFireXD (GemXD).
GemXD operationalized Spark via:
-- True high availability
-- A highly concurrent environment
-- An OLTP engine that can process transactions (mutable state)
With GemXD as a storage engine, we packaged SnappyData with Approximate
Query Processing (AQP) technology. AQP enables interactive response times
even when data volumes are huge because it allows the developer to trade
latency for accuracy. AQP queries (SQL queries with a specified error rate)
execute on sample tables -- tables that have taken a stratified sample of
the full dataset. As such, AQP queries enable much faster decisions when
100% accuracy isn’t needed and sample tables require far fewer resources to
manage.
If that sounds interesting to you, please check out our Github repo (our
release is hosted there under “releases”):
https://github.com/SnappyDataInc/snappydata
We also have a technical paper that dives into the architecture:
http://www.snappydata.io/snappy-industrial
Are you currently using Spark at ****? I’d love to set up a call with you
and hear about how you’re using it and see if SnappyData could be a fit.
In addition to replying to this email, there are many ways to chat with us:
https://github.com/SnappyDataInc/snappydata#community-support
Hope to hear from you,
Pierce
plamb@snappydata.io
http://www.twitter.com/snappydata
Re: Appropriate Apache Users List Uses
Posted by Pierce Lamb <ri...@gmail.com>.
I sent this mail. It was not automated or part of a mass email.
My apologies for misuse.
Pierce
On Tue, Feb 9, 2016 at 12:02 PM, Uwe@Moosheimer.com <Uw...@moosheimer.com>
wrote:
> I wouldn't expect this either.
> Very disappointing...
>
> -Kay-Uwe Moosheimer
>
> Am 09.02.2016 um 20:53 schrieb Ryan Victory <rv...@gmail.com>:
>
> Yeah, a little disappointed with this, I wouldn't expect to be sent
> unsolicited mail based on my membership to this list.
>
> -Ryan Victory
>
> On Tue, Feb 9, 2016 at 1:36 PM, John Omernik <jo...@omernik.com> wrote:
>
>> All, I received this today, is this appropriate list use? Note: This was
>> unsolicited.
>>
>> Thanks
>> John
>>
>>
>>
>> From: Pierce Lamb <pl...@snappydata.io>
>> 11:57 AM (1 hour ago)
>> to me
>>
>> Hi John,
>>
>> I saw you on the Spark Mailing List and noticed you worked for ***** and
>> wanted to reach out. My company, SnappyData, just launched an open source
>> OLTP + OLAP Database built on Spark. Our lead investor is Pivotal, whose
>> largest owner is EMC which makes ***** like a father figure :)
>>
>> SnappyData’s goal is two fold: Operationalize Spark and deliver truly
>> interactive queries. To do this, we first integrated Spark with an
>> in-memory database with a pedigree of production customer deployments:
>> GemFireXD (GemXD).
>>
>> GemXD operationalized Spark via:
>>
>> -- True high availability
>>
>> -- A highly concurrent environment
>>
>> -- An OLTP engine that can process transactions (mutable state)
>>
>> With GemXD as a storage engine, we packaged SnappyData with Approximate
>> Query Processing (AQP) technology. AQP enables interactive response times
>> even when data volumes are huge because it allows the developer to trade
>> latency for accuracy. AQP queries (SQL queries with a specified error rate)
>> execute on sample tables -- tables that have taken a stratified sample of
>> the full dataset. As such, AQP queries enable much faster decisions when
>> 100% accuracy isn’t needed and sample tables require far fewer resources to
>> manage.
>>
>> If that sounds interesting to you, please check out our Github repo (our
>> release is hosted there under “releases”):
>>
>> https://github.com/SnappyDataInc/snappydata
>>
>> We also have a technical paper that dives into the architecture:
>> http://www.snappydata.io/snappy-industrial
>>
>> Are you currently using Spark at ****? I’d love to set up a call with you
>> and hear about how you’re using it and see if SnappyData could be a fit.
>>
>> In addition to replying to this email, there are many ways to chat with
>> us: https://github.com/SnappyDataInc/snappydata#community-support
>>
>> Hope to hear from you,
>>
>> Pierce
>>
>> plamb@snappydata.io
>>
>> http://www.twitter.com/snappydata
>>
>
>
Re: Appropriate Apache Users List Uses
Posted by "Uwe@Moosheimer.com" <Uw...@Moosheimer.com>.
I wouldn't expect this either.
Very disappointing...
-Kay-Uwe Moosheimer
> Am 09.02.2016 um 20:53 schrieb Ryan Victory <rv...@gmail.com>:
>
> Yeah, a little disappointed with this, I wouldn't expect to be sent unsolicited mail based on my membership to this list.
>
> -Ryan Victory
>
>> On Tue, Feb 9, 2016 at 1:36 PM, John Omernik <jo...@omernik.com> wrote:
>> All, I received this today, is this appropriate list use? Note: This was unsolicited.
>>
>> Thanks
>> John
>>
>>
>>
>> From: Pierce Lamb <pl...@snappydata.io>
>>
>> 11:57 AM (1 hour ago)
>>
>>
>>
>> to me
>>
>> Hi John,
>>
>> I saw you on the Spark Mailing List and noticed you worked for ***** and wanted to reach out. My company, SnappyData, just launched an open source OLTP + OLAP Database built on Spark. Our lead investor is Pivotal, whose largest owner is EMC which makes ***** like a father figure :)
>>
>> SnappyData’s goal is two fold: Operationalize Spark and deliver truly interactive queries. To do this, we first integrated Spark with an in-memory database with a pedigree of production customer deployments: GemFireXD (GemXD).
>>
>> GemXD operationalized Spark via:
>> -- True high availability
>> -- A highly concurrent environment
>> -- An OLTP engine that can process transactions (mutable state)
>>
>> With GemXD as a storage engine, we packaged SnappyData with Approximate Query Processing (AQP) technology. AQP enables interactive response times even when data volumes are huge because it allows the developer to trade latency for accuracy. AQP queries (SQL queries with a specified error rate) execute on sample tables -- tables that have taken a stratified sample of the full dataset. As such, AQP queries enable much faster decisions when 100% accuracy isn’t needed and sample tables require far fewer resources to manage.
>>
>> If that sounds interesting to you, please check out our Github repo (our release is hosted there under “releases”):
>> https://github.com/SnappyDataInc/snappydata
>>
>> We also have a technical paper that dives into the architecture: http://www.snappydata.io/snappy-industrial
>>
>> Are you currently using Spark at ****? I’d love to set up a call with you and hear about how you’re using it and see if SnappyData could be a fit.
>>
>> In addition to replying to this email, there are many ways to chat with us: https://github.com/SnappyDataInc/snappydata#community-support
>>
>> Hope to hear from you,
>>
>> Pierce
>> plamb@snappydata.io
>> http://www.twitter.com/snappydata
>
Re: Appropriate Apache Users List Uses
Posted by Ryan Victory <rv...@gmail.com>.
Yeah, a little disappointed with this, I wouldn't expect to be sent
unsolicited mail based on my membership to this list.
-Ryan Victory
On Tue, Feb 9, 2016 at 1:36 PM, John Omernik <jo...@omernik.com> wrote:
> All, I received this today, is this appropriate list use? Note: This was
> unsolicited.
>
> Thanks
> John
>
>
>
> From: Pierce Lamb <pl...@snappydata.io>
> 11:57 AM (1 hour ago)
> to me
>
> Hi John,
>
> I saw you on the Spark Mailing List and noticed you worked for ***** and
> wanted to reach out. My company, SnappyData, just launched an open source
> OLTP + OLAP Database built on Spark. Our lead investor is Pivotal, whose
> largest owner is EMC which makes ***** like a father figure :)
>
> SnappyData’s goal is two fold: Operationalize Spark and deliver truly
> interactive queries. To do this, we first integrated Spark with an
> in-memory database with a pedigree of production customer deployments:
> GemFireXD (GemXD).
>
> GemXD operationalized Spark via:
>
> -- True high availability
>
> -- A highly concurrent environment
>
> -- An OLTP engine that can process transactions (mutable state)
>
> With GemXD as a storage engine, we packaged SnappyData with Approximate
> Query Processing (AQP) technology. AQP enables interactive response times
> even when data volumes are huge because it allows the developer to trade
> latency for accuracy. AQP queries (SQL queries with a specified error rate)
> execute on sample tables -- tables that have taken a stratified sample of
> the full dataset. As such, AQP queries enable much faster decisions when
> 100% accuracy isn’t needed and sample tables require far fewer resources to
> manage.
>
> If that sounds interesting to you, please check out our Github repo (our
> release is hosted there under “releases”):
>
> https://github.com/SnappyDataInc/snappydata
>
> We also have a technical paper that dives into the architecture:
> http://www.snappydata.io/snappy-industrial
>
> Are you currently using Spark at ****? I’d love to set up a call with you
> and hear about how you’re using it and see if SnappyData could be a fit.
>
> In addition to replying to this email, there are many ways to chat with
> us: https://github.com/SnappyDataInc/snappydata#community-support
>
> Hope to hear from you,
>
> Pierce
>
> plamb@snappydata.io
>
> http://www.twitter.com/snappydata
>