Systematic Review of Cloud-Native Data Modeling Techniques Using dbt, Snowflake, and Redshift Platforms

The evolution of cloud-native architectures has transformed data modeling practices, enabling scalable, efficient, and agile data workflows across industries. This systematic review examines contemporary data modeling techniques using dbt (data build tool), Snowflake, and Amazon Redshift, three leading platforms driving cloud-native analytics innovation. Employing the PRISMA methodology, we analyzed peer-reviewed articles, industry whitepapers, and case studies published between 2015 and 2024 to identify emerging trends, best practices, and persistent challenges in cloud-native data modeling. Our findings reveal a significant shift toward modular, declarative, and version-controlled approaches to data modeling, driven largely by dbt’s popularity in managing SQL-based transformations as code. Snowflake’s scalable, multi-cluster shared data architecture and Redshift’s integration with lakehouse models have further democratized complex modeling capabilities across organizations of varying sizes. Key advancements include the use of incremental models to optimize transformation performance, materializations to balance cost and compute efficiency, and advanced schema management techniques to support agile analytics initiatives. However, challenges persist, notably around orchestration complexities, cost governance, data testing rigor, and maintaining model lineage transparency across evolving datasets. The review highlights innovative solutions such as automated documentation, data contracts, metadata-driven modeling, and the adoption of modern DevOps principles (DataOps) to improve reliability and collaboration. Additionally, the integration of machine learning models into cloud-native environments is opening new frontiers for predictive analytics directly within transformation layers. Looking ahead, future research must address the scalability of metadata management, standardized model testing frameworks, and real-time model deployment strategies. This review underscores the need for a unified approach that harmonizes data modeling practices across cloud platforms while maintaining interoperability, transparency, and operational excellence. As data volume and complexity continue to surge, mastering cloud-native data modeling will remain essential for building resilient, intelligent, and scalable analytics ecosystems.