Walmart's real-time inventory use case is uniquely challenging due to its scale and complexity, with the system processing over tens of billions of messages from close to 100 million SKUs in less than three hours. The company leverages Apache Kafka as a key part of its cutting-edge IT architecture to achieve accurate, reliable, and real-time replenishment. To address the challenges of this use case, Walmart has implemented various architectural decisions, including active-passive data replication, resiliency mechanisms, Kafka retries, and fallbacks. The company also optimizes its producer and consumer configurations, such as custom partitioning strategies, linger.ms, batch sizes, and acks, to ensure data consistency and accuracy. Additionally, Walmart has designed a fallback mechanism for when messages cannot be sent to the topic itself, using a rest service to write directly to the database. The system also includes alerts and notifications to monitor its performance and ensure that it meets the company's SLAs.