Abstract: Methods and systems for scaling out real-time computations are disclosed. A stream of event data associated with a plurality of users is received. A set of event-based aggregations to be computed for the plurality of users is determined based on the received stream of event data. User information for the plurality of users is stored in a state store. Computation of the determined set of event-based aggregations is dynamically scaled out using the stored user information. The set of event-based aggregations is computed for the plurality of users from the received stream of event data using the dynamically scaled out computation. The computed event-based aggregations for the plurality of users are stored in the state store.

Abstract: Methods and systems for using a cloud-managed state store are disclosed. A stream of data is received via a network. State information for a real-time computation workload is stored in a cloud-managed state store. The real-time computation workload is scaled out by utilizing the cloud-managed state store to retrieve state information. The stream of data is processed using a processing engine and utilizing the retrieved state information from the cloud-managed state store. Results of processing the stream of data are stored in the cloud-managed state store.