A robust and linearly scalable inventory management system can be developed using distributed systems by avoiding shared mutable state and partitioning inventory by SKU, utilizing partitionable queues. This approach ensures that SKU access is isolated, enhancing visibility across all stores and distribution channels while also enabling lightweight transactions in Cassandra at the partition level. The system employs a Cassandra schema with two tables, inventory.counts and inventory.log, to track actual counts and historical changes, respectively, with features like default TTL and DateTieredCompactionStrategy for efficient data management. Concurrency and synchronization are managed without locks, instead using a chord pattern where single-threaded workers assigned via consistent hashing handle modifications. Two classes of workers—HTTP workers for requests and inventory service workers for database operations—communicate asynchronously through partitioned queues such as Kafka, allowing independent and linear scalability. The queue-based design ensures that workers remain stateless, which allows for easy recovery from crashes and, with transactional queues, guarantees that requests are serviced at least once, enhancing the system's reliability and efficiency.