Real-time inventory management with lambda architecture

Real-time inventory systems play a critical role in preventing stockouts and overstocking, thereby ensuring a positive customer experience. However, creating a real-time inventory API from raw data is complex due to the need for flexible, context-specific data slicing and resource-intensive on-the-fly aggregations. Transactional databases, while often used for maintaining inventory systems, face limitations when managing real-time data at scale, necessitating a more sophisticated approach. Analytical databases, such as Tinybird, offer a solution by using columnar storage and specialized indexing for high-throughput, real-time analytics, though they come with tradeoffs like immutability and costly updates. The lambda architecture emerges as an effective strategy, combining batch and real-time data processing to maintain updated results efficiently, with Tinybird providing a unified platform to streamline this process. By integrating pre-aggregated snapshots with real-time transactional data, Tinybird enables the creation of a dynamic, real-time inventory API that balances historical depth with current data accuracy, allowing businesses to maintain an accurate inventory state seamlessly.