DESIGNING CONTROLLED DATA MIGRATION PIPELINES FROM ON-PREMISES TO CLOUD PLATFORMS FOR MISSION-CRITICAL ENTERPRISE SYSTEMS
DOI:
https://doi.org/10.15662/dc053f78Keywords:
Cloud Migration, Data Migration Pipelines, Mission-Critical Systems, Change Data Capture (CDC), Data Integrity, Hybrid Architecture, Incremental Data Load, ETL/ELT, Data Validation, Zero-Downtime Migration, Enterprise Systems, Secure Data TransferAbstract
The migration of mission-critical enterprise systems from on-premises environments to cloud platforms has become a strategic priority for organizations seeking scalability, resilience, and operational efficiency. However, data migration in such contexts presents significant challenges, including maintaining data integrity, minimizing downtime, ensuring security, and complying with regulatory requirements. This paper proposes a structured and controlledframeworkfor designing data migration pipelines that enable reliable and low-risk transitions to cloud environments.
The proposed approach emphasizes a hybrid and incremental migration model, incorporating initial bulk data transfer followed by continuous synchronization using change data capture mechanisms. The architecture integrates distinct layers for data extraction, transformation, secure transfer, cloud ingestion, and validation, ensuring end-to-end traceability and consistency. Key design principles such as idempotent processing, automated validation, rollback capability, and observability are incorporated to enhance reliability and fault tolerance.
Furthermore, the study outlines migration strategies suitable for mission-critical systems, including phased migration and parallel run approaches, supported by rigorous validation and reconciliation techniques. Performance optimization and security controls are also addressed to ensure efficient and compliant data movement. The paper provides a generalized, vendor-neutral blueprint that can be adapted across industries, enabling enterprises to execute controlled data migration with minimal disruption and high confidence in data accuracy and system continuity.
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