Comparative Analysis Of Data Lakes And Data Warehouses For Machine Learning

The selection of the best data storage and management system stands essential because machine learning (ML) continues expanding its industry-driven innovation. Today's two most prevalent large-scale data processing systems are data lakes and data warehouses, which provide unique strengths and barriers when applied to ML workloads. This paper thoroughly compares data lakes and data warehouses by Analyzing their operational speed, abilities, and price efficiency alongside data, management controls, and ML integration capabilities. Data lakes showcase their superiority in processing unstructured together with semi-structured information because they serve deep learning and big data analytics requirements. A data warehouse offers optimized querying and structured storage, which is suitable for traditional business intelligence applications and ML platforms. The execution speed of data warehouses is faster compared to data lakes, but data lakes enable enhanced real-time abilities and flexibility for large-scale ML work. The research approach consists of conducting a feature-based examination of both architectures, combined with real-world examples, performance scaling data, and cost measurements. This research finds that AI analytics operate most successfully through data lakes; however, structured ML jobs need data warehouses for efficient operation. The combination of data lakehouse technology presents a new possibility for joining both paradigms to create more efficient environments for machine learning applications.