Modern ETL/ELT pipeline design for ML workflows
Year: 2025
Type: article
Abstract: machine learning workflows, examining data processing architectures' evolution and current state. The article explores how organizations are transitioning from traditional ETL to contemporary ELT approaches, driven by the increasing complexity of ML applications and exponential growth in data volumes. The article investigates key aspects including metadata-driven frameworks, quality control mechanisms, performance optimization strategies, and pipeline governance. Through analysis of multiple enterprise implementations, the article demonstrates how modern pipeline architectures have transformed data processing capabilities, improved operational efficiency, and enhanced ML workflow effectiveness. The article also examines emerging challenges in unified processing and schema evolution, providing insights into how organizations are addressing these challenges through advanced architectural patterns and automated management frameworks.
Authors: Srinivasa Sunil Chippada, Shekhar Agrawal
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409251891