This article examines the transformation of data pipeline architectures from traditional batch processing methods to modern real-time and hybrid approaches that meet contemporary business demands. It covers the paradigm shift from ETL to ELT workflows, the emergence of event-driven architectures, and the strategic role of data lakes within comprehensive data management frameworks. By exploring key design principles, including scalability, data quality management, and the critical balance between latency and data integrity, this article provides insights into architectural decisions for various use cases. The article evaluates contemporary technologies, including Apache Airflow, Kafka, and serverless architectures, while offering practical implementation strategies to optimize pipeline efficiency across diverse data ecosystems. Through industry case studies in e-commerce applications, the article demonstrates how organizations leverage different pipeline architectures to enhance customer segmentation, enable dynamic pricing, and strengthen fraud detection capabilities.
Keywords: ETL vs. ELT transformation, data Lakehouse integration, data pipeline architecture, real-time data streaming, serverless data processing
