Cloud & Infrastructure Engineering Case Study:
AI-Integrated Data Migration Framework
Context
A national research and public sector organization faced significant challenges in modernizing its legacy data environment. The institution managed multiple data silos across AWS, Azure, and Google Cloud, leading to inconsistent data governance, slow analytics performance, and high operational overhead. The objective was to establish a unified, cloud-agnostic data migration framework that ensured seamless transfer, validation, and orchestration of massive datasets while maintaining compliance and security.
Challenge
The client’s hybrid data landscape was fragmented, with separate ETL pipelines and non-standard data validation processes.
Specific pain points included:
- High downtime during data migration due to manual intervention.
- Lack of unified visibility across three cloud platforms.
- Absence of AI-driven data quality monitoring.
- Inconsistent governance models and audit traceability across clouds.
SentinelX Approach
SentinelX Digital’s experts architected and delivered a cloud migration and orchestration framework using AI-integrated data pipelines across Google Cloud, Azure, and AWS.
Key steps included:
- Infrastructure-as-Code (IaC) deployment with Terraform for cross-cloud provisioning and repeatability.
- Implementation of AI-based anomaly detection for real-time data validation and quality assurance.
- Kubernetes-based orchestration layer to manage concurrent data flows with zero downtime.
- Deployment of automated data governance policies to ensure consistency and compliance.
- Integration of ML-powered performance forecasting, optimizing network utilization and job scheduling.
Outcomes & Impact
- 40% acceleration in project delivery through intelligent automation of migration workflows.
- Zero downtime achieved during multi-cloud data transfer.
- Improved data accuracy and governance, reducing manual validation by 70%.
- Standardized compliance framework applicable across all three cloud platforms.
Technology Stack
Google Cloud | Azure | AWS | Terraform | Kubernetes | Airflow | Python | MLflow | AI-based anomaly detection models
Disclaimer
This anonymized case study demonstrates SentinelX Digital’s capability in designing and executing AI-driven, multi-cloud data migration programs. All metrics are indicative and representative of comparable delivery outcomes achieved by SentinelX consultants in similar client environments.