Well I used both of those and I have a pretty good idea about their strength's and weaknesses.
If you are solely concentrated in Kafka and you don't have to much experience about stream processing, Kafka Streams is good solution out of the box to help understand the streaming concepts. It Achilles heel in my opinion is its datastore, RockDB to help stateful scenarios with KTable or internal State Stores.
If you use Kafka Streams library, RockDB install itself in the background transparently, which is great for a beginner but troublesome for an experienced developer. RockDB is a key/value database like Cassandra, it has the most strengths of Cassandra but also the weakness, one major of those you can only query the things with primary key, which is for most of the real life scenarios s huge limitation. There are some means to implement your own datastore but they are not that well documented and could be great challenge. Also RockDB is really great loading single Values but if you have iterate over things, after a Dataset size of 100 000 the performance degrades significantly.
Unfortunately while RockDB is embedded so deep in Kafka Streams, it is also not that easy to implement a [CQRS][1] solution with it.
And as mentioned above, it has no concept of Back Pressure while Kafka Consumer give Records one by one, in a scenario that you have to scale out that can be really good bottleneck.
Well so what are the advantages and disadvantages of Akka Streams compared to Kafka Streams. Well first of all, Akka is not that much of out of the box framework, you have to understand the concepts much better, it is not coupled with single [persistence][2] of options, you can choose whatever you want. It has direct support for CQRS pattern ([Akka Projection][3]) so you are not bound to query your data only over Primary Key. Akka developer thought about a lot scaling out and back pressure, committed a lot of code to [Kafka code base][4] to improve performance.
So if you are only working with Kafka and new to Stream Processing you can use Kafka Streams but be prepared that at some point you can hit a wall and switch to Akka Stream.
You want to see working details/example, I have two blogs about it, you can check it those, [blog1][5] [blog2][6]

[1]: https://martinfowler.com/bliki/CQRS.html [2]: https://doc.akka.io/docs/akka/current/typed/persistence.html [3]: https://doc.akka.io/docs/akka-projection/current/overview.html [4]: https://www.lightbend.com/blog/alpakka-kafka-flow-control-optimizations [5]: https://mehmetsalgar.wordpress.com/2022/05/17/alpakka-kafka-stream-and-akka-finite-state-machine/ [6]: https://mehmetsalgar.wordpress.com/2022/04/18/a-model-driven-event-sourced-cloud-ready-application-with-akka-finite-state-machine-using-kafka-cassandra-and-elasticsearch/

Well I used both of those and I have a pretty good idea about their strength's and weaknesses.
If you are solely concentrated in Kafka and you don't have to much experience about stream processing, Kafka Streams is good solution out of the box to help understand the streaming concepts. It Achilles heel in my opinion is its datastore, RockDB to help stateful scenarios with KTable or internal State Stores.
If you use Kafka Streams library, RockDB install itself in the background transparently, which is great for a beginner but troublesome for an experienced developer. RockDB is a key/value database like Cassandra, it has the most strengths of Cassandra but also the weakness, one major of those you can only query the things with primary key, which is for most of the real life scenarios s huge limitation. There are some means to implement your own datastore but they are not that well documented and could be great challenge. Also RockDB is really great loading single Values but if you have iterate over things, after a Dataset size of 100 000 the performance degrades significantly.
Unfortunately while RockDB is embedded so deep in Kafka Streams, it is also not that easy to implement a [CQRS][1] solution with it.
And as mentioned above, it has no concept of Back Pressure while Kafka Consumer give Records one by one, in a scenario that you have to scale out that can be really good bottleneck.
Well so what are the advantages and disadvantages of Akka Streams compared to Kafka Streams. Well first of all, Akka is not that much of out of the box framework, you have to understand the concepts much better, it is not coupled with single [persistence][2] of options, you can choose whatever you want. It has direct support for CQRS pattern ([Akka Projection][3]) so you are not bound to query your data only over Primary Key. Akka developer thought about a lot scaling out and back pressure, committed a lot of code to [Kafka code base][4] to improve performance.
So if you are only working with Kafka and new to Stream Processing you can use Kafka Streams but be prepared that at some point you can hit a wall and switch to Akka Stream.
You want to see working details/example, I have two blogs about it, you can check it those, [blog1][5] [blog2][6]

[1]: https://martinfowler.com/bliki/CQRS.html [2]: https://doc.akka.io/docs/akka/current/typed/persistence.html [3]: https://doc.akka.io/docs/akka-projection/current/overview.html [4]: https://www.lightbend.com/blog/alpakka-kafka-flow-control-optimizations [5]: https://mehmetsalgar.wordpress.com/2022/05/17/alpakka-kafka-stream-and-akka-finite-state-machine/ [6]: https://mehmetsalgar.wordpress.com/2022/04/18/a-model-driven-event-sourced-cloud-ready-application-with-akka-finite-state-machine-using-kafka-cassandra-and-elasticsearch/