Exploring the Convergence of Cloud Computing and Data Warehousing for Smarter Technology Solutions

The paper analyzes cloud computing solutions that boost data warehouses through enhanced scalability combined with cost optimization features together with real-time analytic capabilities. Data warehousing systems that operate from on-site locations struggle with expenses that are high and encounter limitations concerning scalability together with slow processing of data. Organizations choose cloud-based data warehousing solutions because they obtain scalable management capabilities for large datasets which help decrease their infrastructure expenses. The research investigates the cloud-native stack made up of Databricks and Delta Lake and Apache Spark to analyze their capacity for scalable automated data processing capabilities. Organizations can use cloud services AWS, Azure and Google Cloud to process data types with or without structure along with machine learning capabilities and deliver real-time analytics. Performance benchmarks show that Databricks accompanied by Delta Lake performs quicker with less expense than standard data platforms including Snowflake, Redshift and BigQuery. The research establishes how cloud-based data warehousing improves operational effectiveness and lowers total costs with an overview of protection issues in addition to cross-cloud management considerations. Companies achieve enhanced enterprise decision-making through real-time insights delivered by cloud-native data warehousing solutions in their quest to survive in present-day data-intensive markets.