Edge-to-Cloud Data Engineering for Retail Inventory
Keywords:
Smart retail, inventory tracking, edge computing, Internet of Things, distributed data engineering, data streaming, event-driven architecture, lakehouse, lake architecture.Abstract
Inventory tracking is crucial for effective retail operations that rely on physical stocks. However, tracking is not simply about maintaining a current state of product stocks but also involves the management of transactions and the analysis of the past. Hence, the data engineering needs for inventory tracking require a distributed architecture that groups functionality into a set of data processing sub‐systems connected through a set of shared data flows. Distributed data engineering architectures are examined in terms of all‐in‐one data streaming and lakehouse designs as well as more modular event‐driven designs based on typical data streams for enterprise applications and data lakehouse semantics. Further discussion concerns product and stock data models as well as the elements needed for channel management in a sales system.
Retailers need to take many measures to ensure smooth shopping experiences for their customers. Among these critical actions, being able to manually or automatically monitor the state of physical stocks in a store is important to satisfy customer visits. The concept of inventory tracking is linked to this need; however, inventory tracking is not simply about maintaining the current state of product stocks. Rather, it includes management of sold and purchased stocks, background processes that update the stocks, and supporting transactional data. Being able to evaluate whether a certain product was made available for sale in time and under what conditions during its exposure time is crucial information for taking stock and sales channel decisions. Hence, the data engineering needs for inventory tracking require a distributed architecture that groups functionality into a set of data processing sub‐systems connected through a set of shared data flows.
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