I'd also add that PipelineDB excels at SQL-based workloads where you know the queries you want to run in advance and want to stream large volumes of data into PipelineDB's continuous query engine and store ONLY the results of the continuous queries in PipelineDB's underlying relational database. The main value is in continuous computation and distillation for realtime reporting and realtime monitoring and alerting use cases. PipelineDB isn't designed to do ad hoc, exploratory queries, although it can to the same extent that PostgreSQL 9.5 can. It's designed for scenarios where you know the analytic queries you want to run in advance and where a SQL-based approach to streaming analytics with integrated storage provides value by being simpler than building a custom system in Java / Scala using frameworks like Storm, Spark Streaming, Druid, Cassandra / HBase, etc.