Architecting Analytics-Driven Mobile Ecosystems: Scalable Backend Frameworks for Intelligent Data Flow and Real-Time User Insights
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P109Keywords:
Scalable Backend, Real-Time Data, Data Pipelines, Microservices, Apache Kafka, Data Lake, Federated LearningAbstract
Exponential expansion in mobile apps and connected devices has produced mountains of data, which, in turn, require the creation of scalable and smart backend architectures. Such ecosystems need not only to provide real-time analytics but also to be capable of driving actionable insights to maximize user engagement and dynamic application performance. The architectural problem is creating backend systems that are robust, scalable, and smart enough to handle large data streams asynchronously with very little latency while ensuring consistency, reliability, and security. This paper introduces a layered, modular analytical model for analytics-instrumented mobile ecosystems that optimally gather, manipulate, and process data. Our architecture is for microservices, serverless functions, distributed databases, and edge computing components. We present a scalable and fault-tolerant architecture for modern mobile applications using AI/ML algorithms and real-time processing platforms for events like Apache Kafka, Apache Flink, and AWS Kinesis. We then continue to provide a comprehensive methodology of implementation that includes intelligent data pipelines, scalable data lakes, real-time dashboards, and user insight modules. Our prototype implementation is evaluated using latency, throughput, scalability, and predictive accuracy-based benchmarks. The results indicate a 47% increase in latency and a 60% improvement in throughput compared to traditional monolithic architectures when static mobile scenarios are used. In addition, we discuss how analytics feedback loops help facilitate smart decision-making through personalized recommendations, anomaly detection, and user churn prediction. Other issues with data governance, security, and compliance are identified in the paper and foreseen future improvements with federated learning and privacy-preserving analytics. This blueprint is for mobile backend architects and data engineers who want to develop intelligent, scalable, and real-time analytics ecosystems
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