All, I really appreciate anyone's input on this. We are having a very simple traditional OLAP query processing use case. Our use case is as follows.
1. We have a customer sales order table data coming from RDBMs table. 2. There are many dimension columns in the sales order table. For each of those dimensions, we have individual dimension tables that stores the dimension record sets. 3. We also have some BI like hierarchies that is defined for dimension data set. What we want for business users is as follows.? 1. We wanted to show some aggregated values from sales Order transaction table columns. 2. User would like to filter these with specific dimension values from dimension table. 3. User should be able to drill down from higher level to lower level by traversing hierarchy on dimension We want these use actions respond within 2 to 5 seconds. We are thinking about using SPARK as our backend enginee to sever data to these front end application. Has anyone tried using SPARK for these kind of use cases. These are all traditional use cases in BI space. If so, can SPARK respond to these queries with in 2 to 5 seconds for large data sets. Thanks, Renga -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Is-SPARK-is-the-right-choice-for-traditional-OLAP-query-processing-tp23921.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
