Hybrid QA Environments for Cloud-Native Big Data Testing (AWS + Databricks)

The emergence of cloud-native systems and big data systems has resulted in the fact that a strong and scalable quality assurance (QA) system is required, which is capable of operating effectively in a heterogeneous environment. This review explores the use of Amazon Web Services (AWS) and Databricks in mixed-design QA frameworks, including architectural designs, real-time information validation, scaling ETL, and systems that are AI-friendly. The paper is premised on ten contemporary academic and technical resources and explains how hybrid QA systems enhance data dependability, schema enforcement, automation of anomaly detection, and continuous testing in the dynamic world of clouds. The adoption of lakehouse architectures, serverless ETL, automation based on Kubernetes, and declarative validation pipelines are some of the significant topics introduced. The synthesis provides useful details regarding the development of strong QA systems that meet the shifting demands of cloud-native big data systems, which offers a strategic roadmap for businesses that are likely to ensure data quality, data governance, and operational integrity.