Transferring Heterogeneous Database Data to Relational Systems Using HTTP and JSON

Turky, Yousra Atalla

doi:10.26706/ijceae.7.1.20260101

Abstract

The need for efficient access to data distributed across heterogeneous database structures without tight coupling, VPN dependency, or on-premise implementation has become increasingly important for organizations. This paper discusses linking heterogeneous source databases with a centralized relational database using standard web technologies. A lightweight and scalable data transfer platform based on HTTP communication, RESTful services, and JSON serialization is proposed. The design isolates database-specific logic through abstraction and configuration-based schema mapping, enabling easy adaptation to different database types. A C# prototype demonstrates the feasibility of achieving reliable data transfer and schema replication using existing protocols and frameworks. Experimental results indicate effectiveness for periodic data synchronization in cloud-based decision support systems, while highlighting future improvement areas such as security, change tracking, and scalability.

Keywords

Heterogeneous databases Data integration RESTful services HTTP JSON Database synchronization Cloud data transfer

References

I. M. Putrama and P. Martinek, “Heterogeneous data integration: Challenges and opportunities,” Data in Brief, 2024. [Online]. Available: ResearchGate
“Heterogeneous Data Integration: A Literature Scope Review,” Scope Review, 2025. [Online]. Available: ResearchGate
B. Bossenko et al., “Interoperability of health data using FHIR Mapping Language: Transforming HL7 CDA to FHIR,” Frontiers in Digital Health, 2024.
T. Wörden et al., “Mapping hierarchical file structures to semantic data models for efficient data integration,” Data, MDPI, 2024.
“Generic and queryable data integration schema for transcriptomics and epigenomics studies,” Computational and Structural Biotechnology Journal, 2024.
“Intelligent schema mapping methodology,” Journal of Information Systems Engineering and Management, 2025.
“AI-assisted JSON schema creation and mapping,” in Proc. ACM/IEEE MODELS’25, 2025. [Online]. Available: arXiv
“Towards scalable schema mapping using large language models,” in Proc. ACM, 2025.
“A GenAI system for improved FAIR independent biological data integration,” in Proc. ACM, 2025.
“Semantic data integration and querying: A survey and challenges,” ACM Computing Surveys.
Y. Haruki et al., “Contextual graph embeddings: Accounting for data characteristics in heterogeneous data integration,” 2025. [Online]. Available: arXiv
M. Hofer and E. Rahm, “KGpipe: Generation and evaluation of pipelines for data integration into knowledge graphs,” 2025. [Online]. Available: arXiv
M. H. Moslemi et al., “Heterogeneity in entity matching: A survey and experimental analysis,” 2025. [Online]. Available: arXiv
E. Sheetrit et al., “ReMatch: Retrieval-enhanced schema matching with large language models,” 2024. [Online]. Available: arXiv
“Interoperability of heterogeneous health information systems: A systematic literature review,” BMC Medical Informatics and Decision Making, 2023.
“Heterogeneous data integration: A literature scope review,” SciTePress, 2024.
“Data integration,” Journal of Information Systems Engineering and Management, 2025.
“Uniform data access platform for SQL and NoSQL database systems,” ScienceDirect.
“JSON integration in relational database systems,” International Journal of Computer Applications, 2017.
I. M. Putrama and P. Martinek, “Heterogeneous data integration challenges,” Data in Brief. [Online]. Available: ResearchGate
“Dataspace,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Dataspace
“Schema-agnostic databases,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Schema-agnostic_database
“Federated database system,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Federated_database_system
“Model Context Protocol (MCP),” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Model_Context_Protocol
“LinkML,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/LinkML
“XML schema mappings for heterogeneous database access,” ScienceDirect.
“An Internet of Things platform for heterogeneous data integration,” ScienceDirect, 2025.
“Studies on IoT data integration methodologies and interoperability,” ScienceDirect, 2025.
“Semantic integration in big data contexts,” ResearchGate.
“Schema mapping and entity resolution for heterogeneous datasets,” ACM Digital Library.

[1] I. M. Putrama and P. Martinek, “Heterogeneous data integration: Challenges and opportunities,” Data in Brief, 2024. [Online]. Available: ResearchGate

[2] “Heterogeneous Data Integration: A Literature Scope Review,” Scope Review, 2025. [Online]. Available: ResearchGate

[3] B. Bossenko et al., “Interoperability of health data using FHIR Mapping Language: Transforming HL7 CDA to FHIR,” Frontiers in Digital Health, 2024.

[4] T. Wörden et al., “Mapping hierarchical file structures to semantic data models for efficient data integration,” Data, MDPI, 2024.

[5] “Generic and queryable data integration schema for transcriptomics and epigenomics studies,” Computational and Structural Biotechnology Journal, 2024.

[6] “Intelligent schema mapping methodology,” Journal of Information Systems Engineering and Management, 2025.

[7] “AI-assisted JSON schema creation and mapping,” in Proc. ACM/IEEE MODELS’25, 2025. [Online]. Available: arXiv

[8] “Towards scalable schema mapping using large language models,” in Proc. ACM, 2025.

[9] “A GenAI system for improved FAIR independent biological data integration,” in Proc. ACM, 2025.

[10] “Semantic data integration and querying: A survey and challenges,” ACM Computing Surveys.

[11] Y. Haruki et al., “Contextual graph embeddings: Accounting for data characteristics in heterogeneous data integration,” 2025. [Online]. Available: arXiv

[12] M. Hofer and E. Rahm, “KGpipe: Generation and evaluation of pipelines for data integration into knowledge graphs,” 2025. [Online]. Available: arXiv

[13] M. H. Moslemi et al., “Heterogeneity in entity matching: A survey and experimental analysis,” 2025. [Online]. Available: arXiv

[14] E. Sheetrit et al., “ReMatch: Retrieval-enhanced schema matching with large language models,” 2024. [Online]. Available: arXiv

[15] “Interoperability of heterogeneous health information systems: A systematic literature review,” BMC Medical Informatics and Decision Making, 2023.

[16] “Heterogeneous data integration: A literature scope review,” SciTePress, 2024.

[17] “Data integration,” Journal of Information Systems Engineering and Management, 2025.

[18] “Uniform data access platform for SQL and NoSQL database systems,” ScienceDirect.

[19] “JSON integration in relational database systems,” International Journal of Computer Applications, 2017.

[20] I. M. Putrama and P. Martinek, “Heterogeneous data integration challenges,” Data in Brief. [Online]. Available: ResearchGate

[21] “Dataspace,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Dataspace

[22] “Schema-agnostic databases,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Schema-agnostic_database

[23] “Federated database system,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Federated_database_system

[24] “Model Context Protocol (MCP),” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Model_Context_Protocol

[25] “LinkML,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/LinkML

[26] “XML schema mappings for heterogeneous database access,” ScienceDirect.

[27] “An Internet of Things platform for heterogeneous data integration,” ScienceDirect, 2025.

[28] “Studies on IoT data integration methodologies and interoperability,” ScienceDirect, 2025.

[29] “Semantic integration in big data contexts,” ResearchGate.

[30] “Schema mapping and entity resolution for heterogeneous datasets,” ACM Digital Library.