Scaling Kafka Streams for High-Volume Data Processing

What this post added

This post details strategies for scaling Kafka Streams applications for high-volume data processing, focusing on parallelism through partitioning, scaling out vs. scaling up, fine-tuning configuration parameters like num.stream.threads and RocksDB settings, and considerations for stateful vs. stateless applications. It also highlights key monitoring metrics such as consumer lag and CPU utilization.