Strategic Data Engineering in Retail: Building Trustworthy, Cost-Aware,  and ML-Ready Cloud Platforms

Rajesh Sura

Strategic Data Engineering in Retail: Building Trustworthy, Cost-Aware, and ML-Ready Cloud Platforms

Authors

Rajesh Sura

Keywords:

Data engineering, cloud computing, retail analytics, lakehouse, Apache Spark, Airflow, real-time pipelines, Snowflake, data mesh, cloud-native infrastructure, machine learning, demand forecasting, MLOps, predictive analytics

Abstract

The evolution of data engineering in retail has transitioned from batch-oriented ETLparadigms toward agile, scalable, and cloud-native ecosystems. With increasing demand forreal-time personalization, dynamic inventory forecasting, and unified customer experience, retail enterprises are rearchitecting their

References

Ghosh, S., & Saraf, A. (2023). Modern Data Stack in Practice: A Field Guide for Data Engineers. O’Reilly Media.

→ General framework for modern DE platforms, cost, orchestration, and governance.

Batra, R., & Adlakha, M. (2023). Governance in the Modern Data Stack: Lineage, Catalogs, and Contracts. Analytics India Magazine → Discusses lineage tools like DataHub and the rise of data contracts.

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Published

2023-06-15

How to Cite

Rajesh Sura. (2023). Strategic Data Engineering in Retail: Building Trustworthy, Cost-Aware, and ML-Ready Cloud Platforms. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2039–2047. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3922