International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | An Intelligent API-Driven Weather Monitoring Dashboard with Automated CI/CD Deployment on AWS Cloud Infrastructure |
|---|---|
| ABSTRACT | Real-time weather intelligence has become indispensable for applications ranging from logistics and agriculture to emergency response systems. Existing solutions often rely on proprietary vendor lock-in, lack resilient multi-provider fallback mechanisms, or are deployed without reproducible and automated software delivery pipelines. This paper presents the design, implementation, and evaluation of a production-grade, API-centric weather intelligence dashboard built upon a modular Python Flask backend integrated with dual external meteorological data providers OpenWeatherMap and the key-free Open-Meteo service. The system incorporates a bounded Least Recently Used (LRU) time-to-live (TTL) in-memory caching layer to minimize redundant upstream API calls, geolocation services for automatic coordinate-based weather discovery, and a Chart.js-powered responsive frontend supporting skeleton loading states and five-minute auto-refresh cycles. Deployment is orchestrated through a fully automated AWS CI/CD pipeline comprising AWS CodeCommit for source control, AWS CodeBuild for build validation, and AWS Elastic Beanstalk for rolling production deployment, all coordinated by AWS CodePipeline. The proposed architecture achieves sub-300 ms median response latency, a cache hit rate exceeding 82% under moderate load, and a zero-downtime rolling deployment strategy. The framework demonstrates how intelligent provider abstraction, in-memory caching, geolocation enrichment, and cloud-native CI/CD can be unified into a scalable, low-operational-overhead weather intelligence platform suitable for real-world production use. |
| AUTHOR | BOKKA KANAKA VIJAYA LAKSHMI, DR. CHIRAPARAPU SRINIVASARAO PG Scholar, Department of Computer Science, S.V.K.P & Dr. K.S. Raju Arts and Science College (Autonomous), Penugonda, Affiliated to Adikavi Nannaya University, Andhra Pradesh, India Associate Professor, Department of Master of Computer Applications, S.V.K. P & Dr. K. S. Raju Arts and Science College (Autonomous), Penugonda, Affiliated to Adikavi Nannaya University, Andhra Pradesh, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406059 |
| pdf/59_An Intelligent API-Driven Weather Monitoring Dashboard with Automated CI CD Deployment on AWS Cloud Infrastructure.pdf | |
| KEYWORDS | |
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