What are the differences between kappa-architecture and lambda-architecture

Question

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

Answer 1

You may also like to read the original article discussing the two here

Quoting the original blog post

"The efficiency and resource trade-offs between the two approaches are somewhat of a wash. The Lambda Architecture requires running both reprocessing and live processing all the time, whereas what I have proposed only requires running the second copy of the job when you need reprocessing. However, my proposal requires temporarily having 2x the storage space in the output database and requires a database that supports high-volume writes for the re-load. In both cases, the extra load of the reprocessing would likely average out. If you had many such jobs, they wouldn’t all reprocess at once, so on a shared cluster with several dozen such jobs you might budget an extra few percent of capacity for the few jobs that would be actively reprocessing at any given time.

The real advantage isn’t about efficiency at all, but rather about allowing people to develop, test, debug, and operate their systems on top of a single processing framework. So, in cases where simplicity is important, consider this approach as an alternative to the Lambda Architecture."