ETL pipelines for cloud-native data platforms: Architecting real-time analytics on integrated cloud services

This article presents a comprehensive overview of ETL (Extract, Transform, Load) pipelines in cloud-native data platforms, focusing on their architecture and implementation for real-time analytics. It examines how traditional batch-oriented ETL processes have evolved into dynamic, on-demand systems that leverage cloud capabilities to deliver timely insights with enhanced efficiency and reduced operational costs. The discussion covers fundamental components of cloud-native ETL architecture, strategies for real-time data ingestion and transformation, workflow orchestration techniques, and approaches to address key challenges related to data consistency, performance optimization, and security. Throughout the article, architectural patterns and best practices are highlighted to guide organizations in building resilient, scalable ETL pipelines that can adapt to evolving business requirements while enabling actionable analytics at unprecedented speeds.