As cloud data infrastructures expand, organizations face mounting challenges in balancing performance and cost-effectiveness. Inefficient query patterns and unoptimized data retrieval operations often lead to substantial cloud expenditure. This paper proposes a conceptual framework for cloud cost optimization focused on automated query refactoring and materialization strategies. By systematically reviewing peer-reviewed studies, technical reports, and industry best practices from 2015 to 2024, we synthesize critical advances that leverage automation to enhance cloud efficiency without sacrificing analytical depth or data availability. The framework emphasizes two key pillars: (1) automated query refactoring to restructure inefficient SQL or API queries by applying intelligent transformations such as predicate pushdown, join optimization, and selective filtering; and (2) strategic materialization of high-cost query results through techniques like incremental materialized views, cache layering, and cost-based data replication. Special attention is given to how major cloud platforms—including AWS Redshift, GCP BigQuery, and Azure Synapse—enable and support these optimizations via native tools and APIs. Our findings highlight that combining automated query diagnostics with dynamic materialization policies can significantly reduce compute cycles, storage costs, and query latency. Additionally, integrating machine learning models for anomaly detection and pattern recognition into the optimization process further enhances adaptability and cost savings. However, challenges remain, particularly in balancing freshness requirements against materialization overhead and managing complex query dependency graphs. This paper concludes by proposing future directions, such as self-healing query optimization systems, multi-platform orchestration of materialization strategies, and the development of standardized observability frameworks for cloud cost attribution at the query level. In an era where data usage scales exponentially, mastering automated optimization techniques is crucial for organizations seeking to sustain operational efficiency, financial governance, and agile decision-making in dynamic cloud environments.
Ajayi et al. (Sun,) studied this question.