Principles of Building a Fault-Tolerant Data Architecture for Predictive Analytics in Retail

Priyam Das

Citation: Priyam Das, "Principles of Building a Fault-Tolerant Data Architecture for Predictive Analytics in Retail", Universal Library of Engineering Technology, Volume 03, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article is dedicated to the development of principles for building a fault-tolerant data architecture for predictive analytics in retail. The relevance of the study is determined by the growing dependence of retail forecasting, demand planning, and fraud detection systems on real-time streaming infrastructures operating under conditions of partial failures and distributed execution. The scientific novelty lies in the analytical integration of reliability validation, temporal semantics, partition strategy, and hybrid transactional–analytical processing into a unified architectural framework. The work describes structural vulnerabilities of multi-stream joins, watermark misalignment, and failure-induced latency amplification. Special attention is paid to lineage-aware reliability metrics, failure cost modeling, and asynchronous state recovery mechanisms. The goal of the study is to systematize architectural principles that ensure analytical stability under network delays and process crashes. To achieve this goal, comparative analysis, structural synthesis, and source examination were applied. The conclusion substantiates that fault tolerance in retail predictive systems requires coordinated control of event-time processing, partition alignment, and recovery semantics. The article will be useful for data architects, retail analytics engineers, and researchers in distributed systems.


Keywords: Data Quality Pipelines, Distributed Systems, Event-Time Semantics, Fault-Tolerant Architecture, Hybrid Transactional Processing, Retail Predictive Analytics, Stream Processing, Throughput Measurement.

Download doi https://doi.org/10.70315/uloap.ulete.2026.0301019