The retail business is experiencing difficulties in maintaining accurate inventory levels with low latency because of old batch processing systems. The proposed solution is a real-time application that uses the data stream architecture based on event-sourcing with Apache Kafka and Google BigQuery to provide streaming analytics. The immutability of the events makes it possible to reliably stream to Kafka such events as sales, returns, and stock adjustments. The scalability and fault-tolerant design of Kafka and the fact that BigQuery is serverless ensure a persistent foundation and real-time analytics in a sub-second query resolution. Significant improvements were the elaborate schema design, optimal Kafka topic setup, and introduction of real-time dashboards to gain insights into inventory. The results also show less than one-second update latency up to 100,000 events per second, and cost-efficient benchmarks of cloud infrastructure. The solution improves the real-time monitoring of the stock, thus reducing instances of out-of-stock and facilitating better decision making. The possibilities are immense for retail operations, providing better control of inventory management, pricing, and forecasting of demand. This model represents a significant step away from legacy batch ETL processes towards having the correct data as needed within retail to support a high-velocity, globalised sector. Future efforts will be dedicated to multi-region deployment, automatic schema change, and incorporating edge computing in offline shop scenarios.

Event Sourcing, Kafka, BigQuery, Real-Time Analytics, Retail Inventory.

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