lambda-architecture

I'm currently developing an application hosted on Azure that uses Azure Event Hub. Basically I'm sending messages (or should I say, events) to the Event Hub from a Web API, and I have two listeners: a Stream Analytics task for real-time analysis a standard worker role that computes some stuff based on the received events and then stores them into an Azure SQL Database (this is a lambda architecture). I'm currently using the EventProcessorHost library to retrieve my events from the Event Hub inside my worker role. I'm trying to find some best practices about how to use the Event Hub (it is a

If the Kappa-Architecture does analysis on stream directly instead of splitting the data into two streams, where is the datastored then, in a messagin-system like Kafka? or can it be in a database for recomputing? And is a seperate batch layer faster than recomputing with a stream processing engine for batch analytics? "A very simple case to consider is when the algorithms applied to the real-time data and to the historical data are identical. Then it is clearly very beneficial to use the same code base to process historical and real-time data, and therefore to implement the use-case using the