Cloud-native environments have revolutionized backend platform development, offering scalability, resilience, and agility. This review paper examines the evolution of building backend platform capabilities within these environments, focusing on data pipelines and associated tooling. We provide a historical overview of backend architectures, highlighting the shift from monolithic systems to microservices and serverless functions. The core of the review delves into the intricacies of data pipeline design, encompassing data ingestion, transformation, storage, and analysis within cloud-native frameworks. We explore orchestration tools, stream processing engines, and data warehousing solutions essential for managing data flow. Furthermore, we investigate the tooling landscape, examining infrastructure-as-code platforms, containerization technologies (e.g., Docker, Kubernetes), monitoring and observability tools, and CI/CD pipelines. A comparative analysis of different approaches is presented, along with a discussion of current challenges such as data governance, security, and cost optimization. Finally, we outline future research directions, emphasizing the potential of AI-driven data pipelines, edge computing integration, and enhanced automation. This review aims to provide a comprehensive understanding of building robust and scalable backend platform capabilities in cloud-native settings, guiding researchers and practitioners in navigating this complex landscape.

Yingqiang Yuan. Building Backend Platform Capabilities in Cloud-Native Environments: A Data Pipeline and Tooling Perspective. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 1: 70-79. https://doi.org/10.25236/AJCIS.2026.090109.

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