When you deploy spark over hadoop, you typically want to leverage the replication of hdfs or your data is already in hadoop. Again, if your data is already in Cassandra or if you want to do updateable atomic row operations and access to your data as well as run analytic jobs, that may be another case. On Jun 24, 2015 1:17 AM, "commtech" <michael.leon...@opco.com> wrote:
> Hi, > > I work at a large financial institution in New York. We're looking into > Spark and trying to learn more about the deployment/use cases for real-time > analytics with Spark. When would it be better to deploy standalone Spark > versus Spark on top of a more comprehensive data management layer (Hadoop, > Cassandra, MongoDB, etc.)? If you do deploy on top of one of these, are > there different use cases where one of these database management layers are > better or worse? > > Any color would be very helpful. Thank you in advance. > > Sincerely, > Michael > > > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/When-to-use-underlying-data-management-layer-versus-standalone-Spark-tp23455.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > >
