Cloud-Native ETL Workflows using Snowflake and BigQuery for Scalable Product Intelligence

Cloud-native Extract, Transform, Load (ETL) workflows have been incorporated into contemporary product intelligence strategies as an instrument that allows building scalable, automated, and versatile data variables integration pipelines. Combined with tools like Snowflake and BigQuery, organizations will be able to analyze massive clusters of data, enable real-time decision-making, and even preemptive intelligence without the constraints of their ancient systems. This paper will discuss the architecture and installation of cloud-native ETL workflows and how they allow the creation of scalable product intelligence frameworks. It discusses its advantages, such as elasticity, automation, and integration with advanced analytics, and issues concerned with data governance and performance optimization, and control. Based on the analysis of contemporary literature, this paper proposes the strategies of optimizing ETL processes at these platforms that may be used to facilitate product innovation and operating efficiency in the changing business context.