The accelerating digital transformation of retail enterprises has intensified scholarly and practical interest in the convergence of Site Reliability Engineering (SRE) and observability, particularly as organizations migrate from legacy infrastructures to hybrid and cloud-native environments. While modern cloud platforms offer unprecedented scalability and flexibility, retail organizations remain constrained by deeply embedded legacy systems that were not designed for real-time resilience, automated recovery, or fine-grained operational insight. This article develops an extensive, theoretically grounded, and empirically informed analysis of how SRE principles can be systematically implemented within such constrained environments, with observability serving as both an enabling capability and a methodological lens for reliability governance. Drawing exclusively on established literature in SRE, observability, cloud monitoring, and data-driven operations, the study synthesizes conceptual frameworks, methodological approaches, and interpretive findings to address a persistent gap in the literature: the lack of integrative, end-to-end models for applying SRE in legacy retail contexts without disruptive re-platforming.
The article positions observability not merely as an extension of monitoring but as a socio-technical epistemology that reshapes how reliability is defined, measured, and operationalized across organizational boundaries. Building on prior research into metrics, logs, and traces, as well as advances in AI-enhanced monitoring and distributed tracing, the analysis demonstrates how observability infrastructures enable the practical realization of SRE constructs such as service level indicators, error budgets, and blameless postmortems in heterogeneous system landscapes. Particular emphasis is placed on the retail domain, where seasonality, transactional volatility, and customer-facing latency sensitivities magnify the consequences of system unreliability. Through a detailed methodological exposition and an interpretive results section grounded in comparative literature analysis, the study elucidates patterns of reliability improvement, organizational learning, and risk redistribution associated with SRE adoption in legacy-heavy environments.
The discussion advances a critical synthesis of competing scholarly viewpoints, addressing tensions between automation and human judgment, predictive analytics and operational uncertainty, and standardization versus contextual adaptation. Limitations related to data heterogeneity, tool interoperability, and organizational inertia are examined alongside future research directions, including causal inference in observability data and ethical considerations in AI-driven operations. By integrating theoretical depth with domain-specific analysis, this article contributes a comprehensive academic foundation for researchers and practitioners seeking to reconcile legacy constraints with contemporary reliability engineering paradigms.

Site Reliability Engineering, Observability, Legacy Systems, Retail Infrastructure

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