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, u...@moosheimer.com <u...@moosheimer.com> wrote: > I wouldn't expect this either. > Very disappointing... > > -Kay-Uwe Moosheimer > > Am 09.02.2016 um 20:53 schrieb Ryan Victory <rvict...@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 <j...@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 >> >> pl...@snappydata.io >> >> http://www.twitter.com/snappydata >> > >
