Hybrid Data Integration Architectures: Combining Informatica and Cloud-Native Services for Scalable Enterprise Data Systems

Abstract

The exponential growth of enterprise data, driven by digital transformation and distributed systems, has necessitated the development of scalable and flexible data integration architectures. Traditional data integration tools, such as Informatica, offer robust ETL capabilities but often face limitations in handling real-time data processing and cloud scalability requirements. Conversely, cloud-native services provide elasticity, event-driven processing, and seamless integration with modern data ecosystems, yet may lack the maturity and governance features of established platforms. This proposes a hybrid data integration architecture that strategically combines Informatica’s enterprise-grade data management capabilities with cloud-native services such as serverless computing, microservices, and distributed data pipelines. The proposed framework leverages batch and real-time processing, enabling efficient data ingestion, transformation, and orchestration across heterogeneous environments, including on-premises and multi-cloud infrastructures. The architecture emphasizes scalability, fault tolerance, and cost optimization through dynamic resource allocation and intelligent workload distribution. Additionally, it incorporates data governance, security, and compliance mechanisms to ensure data integrity and regulatory adherence. Experimental evaluation demonstrates improved performance, reduced latency, and enhanced system flexibility compared to traditional monolithic integration approaches. The study concludes that hybrid architectures represent a practical and future-ready solution for enterprise data integration, enabling organizations to balance legacy system reliability with the agility and scalability of cloud-native technologies.