Apache Kafka includes Kafka Streams, a Java and Scala client library designed for building scalable applications and microservices that process data in Kafka. Kafka Streams supports both stateless operations, such as maps and filters, and stateful operations, like windowed joins and aggregations, using local state stores backed by RocksDB for fault tolerance. RocksDB, a key-value store developed by Facebook, is optimized for high performance and is configured to use level or universal compaction to manage data size and write amplification. Kafka Streams distributes tasks across partitions, using a topology of processors connected by streams, where stateful processors utilize state stores that are fault-tolerant due to changelog topics in Kafka. By default, Kafka Streams employs universal compaction but can be adjusted to level compaction to manage disk usage. Operational challenges such as high memory, disk usage, or write stalls can be addressed by tuning RocksDB configurations, like adjusting memtable sizes, block cache, compaction styles, and the number of open files. These configurations help maintain efficient performance and fault tolerance in Kafka Streams applications.