Scalable Cloud Architectures for Efficient Processing of Multi-Structured Big Data

Large Language Models (LLMs) now dominate the field of intelligent cloud-native applications by providing strong generative functionality with context-aware automated operations. The achievement of large-scale benefits from LLMs depends on implementing an integrated data engineering solution which combines multi-source data pipelines and live data processing while supporting operational governance measures and maximizing scalability and cost effectiveness. This paper develops an integrated data engineering system designed for cloud applications using LLMs which applies modular design patterns with distributed control systems and automated pipeline optimization algorithms. The framework combines data ingestion along with feature transformation and metadata management capabilities and LLM-centric model services that incorporate Mops and Limos practices. Here it is explained both the design structure of the system alongside deployment tactics and measurement standards which support higher operational output and processing capacity. The system provides multiple cloud platform capabilities including AWS Azure and GCP so businesses can deploy LLMs in multi-tenant environments with standardized data management and security features. Experimental assessments prove the framework supplies scalability while minimizing latency and achieving operational stability which prepares it as a base for future-generation enterprise AI applications.