Cloud-Native Business Intelligence Transformation: Migrating Legacy Systems to Modern Analytics Stacks for Scalable Decision-Making

The shift from legacy business intelligence (BI) systems to cloud-native analytics stacks marks a critical juncture in the evolution of data-driven decision-making. Traditional BI architectures—rooted in on-premise data warehouses, rigid ETL pipelines, and siloed reporting tools—are increasingly unable to meet the demands of modern enterprises that require real-time insights, horizontal scalability, and cost-efficiency. As organizations contend with rapidly growing data volumes and the need for operational agility, the transformation toward cloud-native BI has emerged as both a technological necessity and a strategic enabler. Cloud-native BI leverages distributed computing, elastic storage, and modular orchestration frameworks to deliver scalable, flexible, and resilient data pipelines. Platforms such as Amazon Redshift, Google BigQuery, and Snowflake—coupled with modern tools like dbt, Apache Airflow, and Looker—facilitate a decoupled, event-driven architecture that supports both batch and streaming analytics. This enables teams to automate transformations, improve observability, and deliver faster insights to business stakeholders, thereby enhancing data usability across the organization. This explores the architectural principles, migration strategies, and implementation best practices required to modernize legacy BI environments. It examines key challenges such as data migration, pipeline refactoring, cost management, and change enablement. Furthermore, it presents real-world case studies that illustrate performance improvements, reduced latency, and total cost of ownership (TCO) benefits achieved through cloud-native modernization. By synthesizing practical insights with technical depth, this paper offers a comprehensive framework for enterprise architects, data engineers, and analytics leaders seeking to future-proof their BI infrastructure. It argues that cloud-native transformation is not simply a matter of lifting and shifting legacy systems but requires a holistic reengineering of data culture, tooling, and governance. Ultimately, the transition enables scalable, data-driven decision-making aligned with the speed and complexity of the digital age.