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
