Something simple as that should not require tons of RAM and CPU because of Java. Writing it in natively compiled language would produce a lean product - Rust, Go, etc.
Almost every company doing real time/stream processing at the scale of TB/hour or greater is doing so by relying on Apache Kafka, written in Java and Scala.
I've personally been a member of a team that wrote several services (network collectors, observability processing) in Go/Rust/JVM (we preferred Kotlin) in parallel for performance comparisons and found the JVM services to show much better throughput.
Your perspective seems quite outdated. Possibly from before Go or Rust even existed?
[1] https://adamdrake.com/command-line-tools-can-be-235x-faster-...
2. Hadoop is used for batch processing and it can be kind of slow, that's true. However the whole point of Hadoop is that you can operate on actually big data, not the 2GB database in the blog post. If it fits on one HDD then you don't need Hadoop and it will just slow you down!
Distributed big data processing systems need big data to actually be useful. Small data that fits on a single machine can also be processed on a single machine, which will always be faster than using a cluster with orchestration, distribution and network overhead.
... Java?
You can natively compile Java these days.