DATA SYNCHRONIZATION IN E-COMMERCE: BUILDING SCALABLE SYSTEMS FOR AI-DRIVEN PERSONALIZATION
Keywords:
Data Synchronization, E-commerce Infrastructure, AI-Driven Personalization, Distributed Systems, Real-time ProcessingAbstract
Data synchronization in e-commerce has become critical for enabling real-time analytics and personalized customer experiences across retail platforms. This article explores the infrastructure and methodologies required to implement scalable data synchronization frameworks, examining core technologies such as Change Data Capture, stream processing, and distributed data pipelines. The article investigates key components, including metadata management, schema evolution, and fault-tolerant architectures, while highlighting their integration with AI workflows for recommendation systems and inventory optimization. Technical challenges such as latency management, system heterogeneity, and distributed consistency are analyzed, providing insights into building robust synchronization systems. The article also presents implementation best practices for monitoring, observability, and fault tolerance, offering comprehensive guidance for organizations building modern e-commerce platforms.
References
J.P. Morgan, "Global e-commerce trends report." [Online]. Available: https://www.jpmorgan.com/content/dam/jpm/treasury-services/documents/global-e-commerce-trends-report.pdf
Marcus Basalla et al., "On Latency of E-Commerce Platforms," ResearchGate, January 2021. [Online]. Available: https://www.researchgate.net/publication/350600983_On_Latency_of_E-Commerce_Platforms
Dhamotharan Seenivasan, Muthukumaran Vaithianathan, "Real-Time Adaptation: Change Data Capture in Modern Computer Architecture," ESP International Journal of Advancements in Computational Technology, Volume 1 Issue 2 September 2023. [Online]. Available: https://www.espjournals.org/IJACT/2023/Volume1-Issue2/IJACT-V1I2P106.pdf
Mitch Cherniack et al., "Scalable Distributed Stream Processing," ResearchGate, January 2003. [Online]. Available: https://www.researchgate.net/publication/220988218_Scalable_Distributed_Stream_Processing
Chandan Gaur, "Modern Data Warehouse Architecture and its Best Practices," XenonStack, 18 November 2024. [Online]. Available: https://www.xenonstack.com/insights/what-is-a-modern-data-warehouse
Dirk V. Arnold, Hans-Georg Beyer, "Performance analysis of evolution strategies with multi-recombination in high-dimensional RN-search spaces disturbed by noise," Theoretical Computer Science, Volume 289, Issue 1, 23 October 2002, Pages 629-647. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S030439750100384X?via%3Dihub
Valentyna Pleskach, "An E-Commerce Recommendation Systems Based on Analysis of Consumer Behavior Models," International Scientific Symposium «Intelligent Solutions» IntSol-2023, September 27–28, 2023. [Online]. Available: https://ceur-ws.org/Vol-3538/Paper_19.pdf
Özge Albayrak Ünal, Burak Erkayman & Bilal Usanmaz, "Applications of Artificial Intelligence in Inventory Management: A Systematic Review of the Literature," Archives of Computational Methods in Engineering, Volume 30, pages 2605–2625, (2023). [Online]. Available: https://link.springer.com/article/10.1007/s11831-022-09879-5
Seo Jin Park, "Achieving Both Low Latency and Strong Consistency in Large-Scale Systems," Stanford University Technical Report, October 2019. [Online]. Available: https://web.stanford.edu/~ouster/cgi-bin/papers/ParkPhD.pdf
Nishanth Reddy Mandala, "Data Integration in Heterogeneous Systems," ESP Journal of Engineering & Technology Advancements, Volume 2 Issue 4, December 2022. [Online]. Available: https://www.espjeta.org/Volume2-Issue4/JETA-V2I4P122.pdf
H. Samarrokhi, "Monitoring & Observability in Distributed Systems," LinkedIn, January 30, 2020. [Online]. Available: https://www.linkedin.com/pulse/monitoring-observability-distributed-systems-hossein-samarrokhi
GeeksforGeeks, "Fault Tolerance in Distributed System," 01 Aug, 2024. [Online]. Available: https://www.geeksforgeeks.org/fault-tolerance-in-distributed-system/
