I think streaming-first systems like Flink are best positioned to answer your need. You might need to abstract the query or subscription infrastructure into yo

I think streaming-first systems like Flink are best positioned to answer your need. You might need to abstract the query or subscription infrastructure into your data model though: Subscriptions: INSERT INTO user_orders_total_feed(user_id, data) SELECT uo.user_id, SUM(o.price) total FROM user_order uo JOIN order o ON uo.user_id = o.user_id JOIN user_live_subscription uls ON uo.user_id = uls.user_id GROUP BY uo.user_id WHERE uls.subscription_topic = 'orders_total' And for queries you would join on a temporal snapshot using 'FOR SYSTEM TIME AS OF' [1] You could have your data fully stored in Kafka or Pulsar, Kappa-architecture-style ; or you could have it in regular rdbms but with Change Data Capture on top [2] (basically shipping your WAL to Kafka/Pulsar) The good thing about Kafka and Flink is that they scale. Your regular db might not sustain the query rate if every query needs a write to a table, but Kafka/Flink will. [1] https://ci.apache.org/projects/flink/flink-docs-stable/dev/table/streaming/joins.html#processing-time-temporal-join https://ci.apache.org/projects/flink/flink-docs-stable/dev/t... [2] https://debezium.io/ https://debezium.io/