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Zenodo
2025
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| Online-Zugang: | https://doi.org/10.5281/zenodo.17888899 |
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| _version_ | 1866901753126977536 |
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| author | Tiwari, Aniruddh |
| author_facet | Tiwari, Aniruddh |
| contents | <p>Modern enterprises increasingly operate in environments where multiple business domains, functional units, operational groups, or external partners must share common data infrastructure while maintaining strict isolation, governance, and performance guarantees. Traditional data warehouses and lakehouse platforms, designed for monolithic or single-tenant usage, struggle to address the diversity of requirements introduced by heterogeneous tenant workloads, varying consumption patterns, and complex governance boundaries. As organizations embrace shared analytical platforms to reduce cost and accelerate insight generation, the need for scalable, secure, resource-efficient, multi-tenant data architectures has become critical.</p> <p>This white paper introduces the Multi-Tenant Distributed Data Architecture Framework (MTD-DAF), a high-level architectural model conceptualized and authored by Aniruddh Tiwari. MTD-DAF offers a unified conceptual blueprint for constructing modern enterprise data warehouse and lakehouse systems capable of supporting tenant-segregated data operations while leveraging common infrastructure. The framework outlines architectural principles for tenant isolation, workload differentiation, metadata-driven orchestration, distributed resource governance, and cross-tenant observability—entirely at a conceptual level without disclosing implementation details.</p> <p>MTD-DAF addresses long-standing challenges in multi-tenant environments, including inconsistent governance boundaries, inefficient resource utilization, data leakage risks, and difficulty maintaining simultaneous performance guarantees for diverse workloads. Its principles have informed enterprise modernization efforts across industries and contributed to evolving best practices in data engineering and distributed system design.</p> <p><span>This paper offers a patent-safe description of MTD-DAF’s conceptual structure, enterprise value, and industry applicability. It highlights the originality, significance, and cross-sector impact of the framework, positioning it as a meaningful contribution to the field of data warehousing, lakehouse engineering, and distributed data architecture.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17888899 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The Multi-Tenant Distributed Data Architecture Framework (MTD-DAF): A High-Level Architecture for Scalable, Segregated, and Efficient Enterprise Lakehouse Systems Tiwari, Aniruddh <p>Modern enterprises increasingly operate in environments where multiple business domains, functional units, operational groups, or external partners must share common data infrastructure while maintaining strict isolation, governance, and performance guarantees. Traditional data warehouses and lakehouse platforms, designed for monolithic or single-tenant usage, struggle to address the diversity of requirements introduced by heterogeneous tenant workloads, varying consumption patterns, and complex governance boundaries. As organizations embrace shared analytical platforms to reduce cost and accelerate insight generation, the need for scalable, secure, resource-efficient, multi-tenant data architectures has become critical.</p> <p>This white paper introduces the Multi-Tenant Distributed Data Architecture Framework (MTD-DAF), a high-level architectural model conceptualized and authored by Aniruddh Tiwari. MTD-DAF offers a unified conceptual blueprint for constructing modern enterprise data warehouse and lakehouse systems capable of supporting tenant-segregated data operations while leveraging common infrastructure. The framework outlines architectural principles for tenant isolation, workload differentiation, metadata-driven orchestration, distributed resource governance, and cross-tenant observability—entirely at a conceptual level without disclosing implementation details.</p> <p>MTD-DAF addresses long-standing challenges in multi-tenant environments, including inconsistent governance boundaries, inefficient resource utilization, data leakage risks, and difficulty maintaining simultaneous performance guarantees for diverse workloads. Its principles have informed enterprise modernization efforts across industries and contributed to evolving best practices in data engineering and distributed system design.</p> <p><span>This paper offers a patent-safe description of MTD-DAF’s conceptual structure, enterprise value, and industry applicability. It highlights the originality, significance, and cross-sector impact of the framework, positioning it as a meaningful contribution to the field of data warehousing, lakehouse engineering, and distributed data architecture.</span></p> |
| title | The Multi-Tenant Distributed Data Architecture Framework (MTD-DAF): A High-Level Architecture for Scalable, Segregated, and Efficient Enterprise Lakehouse Systems |
| url | https://doi.org/10.5281/zenodo.17888899 |