EVOLVING DATA ENGINEERING LANDSCAPE: INTEGRATING MODERN DATA STACKS WITH SCALABLE, COST-EFFICIENT DATA LAKES FOR FUTURE AI AND ML NEEDS

Abstract

The data engineering landscape is rapidly transforming to meet the growing demands of AI and machine learning (ML). Traditional monolithic data architectures are being replaced by modular, cloud-native data stacks that prioritize flexibility, scalability, and cost-efficiency. This paper explores the integration of modern data stack components—such as ELT pipelines, real-time data streaming, and cloud data warehouses—with scalable data lakes that serve as unified repositories for structured and unstructured data. We discuss best practices for designing data platforms that can seamlessly support AI/ML workflows, including metadata management, data versioning, governance, and interoperability across tools. Additionally, we analyze cost-performance tradeoffs and architectural patterns that enable organizations to future-proof their data infrastructure while optimizing for real-time analytics, model training, and data democratization. By bridging the gap between modern data stacks and next-generation data lakes, organizations can unlock the full potential of their data to drive innovation in AI and ML.