Modernizing Enterprise Analytics through Low-Code Automation and Cloud-Native Data Architectures

Abstract

Enterprise analytics has undergone significant transformation in recent years, driven by the convergence of low-code automation platforms and cloud-native data architectures. This review examines how organizations are modernizing their analytics capabilities through these technological paradigms. Low-code development platforms (LCDPs) enable rapid application development with minimal hand-coding, democratizing analytics across business units. Simultaneously, cloud-native data architectures leverage containerization, microservices, and serverless computing to provide scalable, resilient infrastructure for data processing and analytics workloads. This paper synthesizes recent literature to explore the synergistic relationship between low-code automation and cloud-native architectures in enterprise analytics modernization. We analyze key technological components including data lakehouse architectures, automated machine learning (AutoML), containerized analytics pipelines, and serverless data processing frameworks. The review identifies critical implementation challenges such as data governance, security concerns, skill gaps, and integration complexities. Furthermore, we examine emerging trends including artificial intelligence (AI)-augmented analytics, edge analytics, and real-time streaming architectures. Our analysis reveals that organizations successfully combining low-code platforms with cloud-native infrastructure achieve faster time-to-insight, reduced development costs, and enhanced analytical democratization. However, successful implementation requires careful consideration of organizational readiness, data architecture maturity, and change management strategies. This review provides comprehensive insights for practitioners and researchers seeking to understand the current state and future directions of enterprise analytics modernization.