Picture a global stock exchange handling millions of transactions per second, where even a millisecond delay can lead to significant financial losses. Poor Kafka optimization causes latency spikes, resulting in delayed data streams that disrupt operations in finance, e-commerce, and IoT industries. Optimizing Kafka for low-latency event streaming is crucial for mission-critical applications. Understanding the sources of latency is essential to optimize Kafka performance. Factors contributing to producer-to-broker latency include acknowledgment settings, batching and buffering delays, compression overhead, and use cases that have reduced transaction speed by 30%. Broker-to-consumer latency is also a significant challenge, with contributors including replication lag, log flushing and segment size, consumer lag accumulation, and use cases that fixed slow replica synchronization issues. Network and infrastructure delays can introduce message delay and increase end-to-end latency, with TCP overhead, disk I/O bottlenecks, JVM garbage collection pauses, and hardware limitations often becoming hidden bottlenecks. To tackle these challenges, businesses must configure their networks to reduce latency, select the right hardware for low-latency Kafka, optimize memory allocation for Kafka brokers, leverage high-speed networking, and use dedicated Kafka observability tools that provide deep visibility into message delays and event streaming. Effective optimization requires a comprehensive observability strategy and tools like Acceldata's Data Observability Platform that offer real-time insights, anomaly detection, and cross-cluster observability to ensure seamless Kafka performance even under high-volume workloads.