[aloknnikhil]

No. The original Kafka paper does talk about logs in the observability sense as a premise to solve the aggregation problem.

https://cs.uwaterloo.ca/~ssalihog/courses/papers/netdb11-fin...

> There is a large amount of “log” data generated at any sizable internet company. This data typically includes (1) user activity events corresponding to logins, pageviews, clicks, “likes”, sharing, comments, and search queries; (2) operational metrics such as service call stack, call latency, errors, and system metrics such as CPU, memory, network, or disk utilization on each machine. Log data has long been a component of analytics used to track user engagement, system utilization, and other metrics.

> We have built a novel messaging system for log processing called Kafka [18] that combines the benefits of traditional log aggregators and messaging systems....Kafka provides an API similar to a messaging system and allows applications to consume log events in real time.

[kasey_junk]

A quote from the LinkedIn blog post linked in the article:

“But before we get too far let me clarify something that is a bit confusing. Every programmer is familiar with another definition of logging—the unstructured error messages or trace info an application might write out to a local file using syslog or log4j. For clarity I will call this "application logging". The application log is a degenerative form of the log concept I am describing”

[aloknnikhil]

Fair enough. But I don't think quoting this for logging in the tracing sense is wrong here. He does acknowledge that trace logs are a degenerative form of logs from the perspective of log processing. The only difference being in the semantics of human readable text v/s binary logs.