Data Integration and Storage Strategies in Heterogeneous Analytical Systems: Architectures, Methods, and Interoperability Challenges

In the current scenario of universal accessibility of data, organisations face highly complex challenges related to integrating and processing diverse sets of data in order to meet their analytical needs. This review paper analyses traditional and innovative methods used for data storage and integration, with particular focus on their implications for scalability, consistency, and interoperability within an analytical ecosystem. In particular, it contributes a cross-layer taxonomy linking integration mechanisms (schema matching, entity resolution, and semantic enrichment) to storage/query substrates (row/column stores, NoSQL, lakehouse, and federation), together with comparative tables and figures that synthesise trade-offs and performance/governance levers. Through schema mapping solutions addressing the challenges brought about by structural heterogeneity, storage architectures varying from traditional storage solutions all the way to cloud storage solutions, and ETL pipeline integration using federated query processors, the research provides specific attention for the application of metadata management, with a focus on semantic enrichment using ontologies and lineage management to enable end-to-end traceability and governance. It also covers performance hotspots and caching techniques, along with consistency trade-offs arising out of distributed systems. Empirical case studies from real applications in enterprise lakehouses, scientific exploration activities, and public governance applications serve to invoke this review. Following this work is the possibility of future directions in convergent analytical platforms with support for multiple workloads, along with metadata-centric orchestration with provisions for AI-based integration. Combining technological advancement with practical considerations results in an enabling resource for researchers and practitioners seeking the creation of fault-tolerant, reliable, and future-ready data infrastructure. This review is primarily aimed at researchers, system architects, and advanced practitioners who design and evaluate heterogeneous analytical platforms. It also offers value to graduate students by serving as a structured overview of contemporary methods, thereby bridging academic knowledge with industrial practice.