Abstract

The role of data engineering in e-commerce systems is critical, especially for personalization, product recommendations, dynamic pricing, and demand forecasting. Conventional data engineering relies on large amounts of manual effort, making it challenging to keep pace with rapidly changing data and application requirements. The application of AI techniques to data pipelines in e-commerce is an active area of research and practice. Such application offers the prospect of greater automation of data engineering, similar to the role of CI/CD and MLOps for software and machine-learning model development. The work summarizes concepts relating to the use of AI techniques in data processing and engineering, thereby extending the scope of AI beyond traditional product systems and closer to the data pipelines themselves. Functionally significant e-commerce application areas are highlighted, with an emphasis on personalization systems. Several emerging architectural patterns in e-commerce, including data meshes, federated learning, privacy-preserving computation, and observability tools, are introduced. These patterns encapsulate concepts that support the deployment and operationalization of AI-driven data-granularity pipelines. Recent research also emphasizes how the application of AI techniques helps capture ever-changing data conditions and customer needs. Academic work proposes measures that can be applied across data pipelines to provide insights about data drift—an accelerated shift in data characteristics that leads to decreased model quality. Practical challenges in real-time usage together with opportunities for the further development of AI techniques in data engineering are also considered.

Keywords

  • Artificial intelligence data engineering data engineering automation data pipeline e-commerce intell

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