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In the era of big data, organizations face challenges in efficiently managing, processing, and deriving insights from massive data volumes.Traditional data architectures often suffer from limitations such as lack of scalability, high latency, and fragmented data silos.To address these issues, this paper explores a unified and scalable data ecosystem by leveraging AI-driven solution architecture, cloud-native technologies, and advanced data analytics methodologies.The proposed architecture integrates key components such as data ingestion, storage, processing, and analytics into a seamless workflow, facilitating real-time decision-making and predictive insights.The study emphasizes the use of Kubernetes for orchestration, Apache Kafka for real-Building a Unified and Scalable Data Ecosystem: AI-Driven Solution Architecture for Cloud Data Analytics https://iaeme.com/Home/journal/IJCET138 editor@iaeme.comtime data streaming, and Databricks Lakehouse for unified data management.Additionally, cloud services like AWS Lambda and Azure Functions enable serverless execution, improving efficiency and cost-effectiveness.Through a modular and microservices-driven approach, this research highlights the benefits of decoupling compute and storage to enhance system elasticity.The findings suggest that AI-driven analytics, coupled with a scalable cloud infrastructure, significantly improve data processing speed, operational efficiency, and business intelligence capabilities.
Polisetty et al. (2022) studied this question.