Authors: Santhosh Reddy BasiReddy
Abstract: Customer Relationship Management platforms rely heavily on high-volume batch processing to support data synchronization, segmentation, analytics enrichment, and operational workflows, all of which demand consistent performance at scale. Traditional on-premises ecosystems often struggle with rigid scheduling, extended runtimes, infrastructure limits, and tight coupling between systems that were never designed for modern data intensity. These constraints slow down downstream analytics, delay customer insights, and restrict the agility of CRM-driven business processes. Cloud-enabled automation pipelines introduce a transformative paradigm by combining distributed computation, horizontally scalable storage, event-driven orchestration, and intelligent workload optimization to dramatically improve processing speed, reliability, and adaptability. By leveraging principles from foundational distributed systems models, this article explores how CRM modernization can be achieved through decoupled architectures, automated pipeline flows, and elastic compute strategies that dynamically adjust to data volume and complexity, enabling organizations to operate CRM ecosystems with greater efficiency, accuracy, and real-time readiness.
DOI: http://doi.org/10.5281/zenodo.18014580
