Enterprise and RAN-Aware Data and Analytics Platforms for Mission-Critical and Low-Latency Digital Services

Enterprise digital service commercial applications are progressing to become constrained by restrictive performance and reliability and latency demands motivated by mission critical work cases like industrial automation, telemedicine, autonomous systems, and real-time operational intelligence. Traditional cloud-based analytics models are typically scalable, but are not typically capable of meeting the requirements of ultra-low-latency and deterministic services introduced by distributed, radio access network (RAN) applications. The paper will include an extensive architectural and an analytical model of Enterprise and RAN-Aware-based Data and Analytics Platforms aimed to serve mission-critical and low-latency digital services. The framework suggested incorporates cloud-native data systems, distributed analytics, streaming data pipelines, and network-aware strategies of computing with a clear understanding of the RAN dynamics. In contrast to conventional enterprise-level data platforms, the RAN-aware model uses radio conditions, network variability, edge resource constraints and latency budgets as first-order design parameters. This study explains why there should be close integration between enterprise analytics layers and live network telemetry to allow placement of adaptive computation, smart workload orchestration, and fault-tolerant distributed processing. The paper presents a multitiered system network with edge computing nodes, regional consolidation planes, and centralized cloud management control planes. The frameworks of data processing utilizes the streaming architecture and low-latency pipelines that are able to perform real-time inferences and decisions. The important innovations are latency adaptive processing models, network aware scheduling functions, and reliability optimized replication strategies. The estimation of latency and workload distribution that aims at describing the system behavior under network uncertainties is provided by mathematical models. Through experimental analysis, we show the improvement of service responsiveness, throughput stability, and fault resilience over cloud-only baselines. The quantitative analysis shows that the application of RAN-aware mechanisms dramatically decreases the tail latency and improves the yield of reliable mission-critical workflows. The findings confirm the usefulness of distributed analytics together with edge-aware RAN intelligence.bThis paper forms part of an emerging effort in the interface between enterprise data engineering, network-aware computing and low-latency distributed systems. The results offer architectural directions on the next-generation enterprise platform which can support the digital services running at scale, in uncertainty, and in heterogeneous network landscapes.