Quick Facts
- Client: Global Supply Chain Services Organization
- Industry: Supply Chain & Logistics
- Partnership: Collaboration with PwC on digital transformation initiative
- Vision: Modernize on-premises data ecosystem to cloud-native platform for scalability and efficiency
- Platform: Microsoft Azure with SingleStore, Apache Airflow, Apache Kafka
- Tech Stack: Azure DevOps, Python, SQL, SAS, Azure Key Vault, Datadog
- Timeline: 18-24 months phased migration program
- Expertise Delivered: Data platform engineering, cloud consulting, DevOps automation, infrastructure monitoring
About the Client
- Client: Global Supply Chain Services Organization
- Industry: Supply Chain & Logistics
- Engagement Model: Partnership with PwC on enterprise digital transformation
- Environment: Highly complex on-premises data environment supporting multiple business domains
- Vision: Modernize infrastructure without disrupting ongoing supply chain operations
The Challenge
The supply chain organization operated a highly complex on-premises data environment supporting critical operational workflows. Key challenges included:
- Legacy infrastructure with limited scalability and high maintenance costs
- Multiple interconnected systems and data pipelines across business domains
- Dependency-heavy workflows requiring careful sequencing during migration
- Tight delivery timelines driven by business priorities
- Need for stronger governance, security, and observability
- Risk of operational disruption during migration
The complexity of maintaining mission-critical supply chain operations while modernizing infrastructure created significant technical and business risks.
Technical Complexity
- Hundreds of interconnected data pipelines across domains
- Complex dependency chains between systems
- Mixed technology stack requiring careful migration planning
- Need to maintain business continuity throughout transformation
Business Requirements
- Zero tolerance for supply chain operational disruptions
- Enhanced scalability for growing data volumes
- Improved security and governance aligned with enterprise standards
- Cost optimization through cloud migration
- Future-ready platform for advanced analytics
Methodology
- Agile delivery practices with week-by-week progress visibility
- Phased migration strategy to minimize risk
- Structured work planning tracking dependencies and milestones
- Close collaboration with client architects and engineering teams
- Continuous assessment and adaptation to evolving business needs
Phases
- Assessment & Planning: System categorization and migration roadmap development
- Platform Design: Cloud-native architecture for operational and analytical workloads
- Data Engineering: Pipeline migration and orchestration implementation
- DevOps Implementation: CI/CD automation and security enablement
- Monitoring Setup: Infrastructure visibility and observability
- Phased Migration: Domain-by-domain system migration execution
Migration Approach
Each system was assessed based on:
- Business criticality
- Data size and complexity
- Number of dependent processes
- Migration effort and risk
This structured assessment enabled a 18-24 month migration roadmap with flexibility to adapt to changing business priorities.
Solution
Cloud-First Data Platform Design
- Cloud-native architecture built on Microsoft Azure
- Elastic scalability for growing data volumes
- Improved performance and reliability over legacy systems
- Secure access and governance aligned with enterprise standards
- Support for both operational and analytical workloads
- Modern cloud data services integration
Data Engineering & Orchestration
- Robust data pipelines for scheduled and batch processing
- SQL-based transformations and stored procedures
- Python-driven workflows for analytics use cases
- Integration with existing enterprise systems and tools
- Apache Airflow for workflow orchestration
- Consistency, reliability, and visibility across all data flows
DevOps & Security Enablement
- CI/CD pipelines using Azure DevOps tooling
- Version-controlled infrastructure and deployment workflows
- Azure Key Vault for secure secrets management
- Role-based access models aligned with enterprise security
- Governance practices meeting compliance requirements
- Significantly reduced manual intervention in deployments
Infrastructure Monitoring & Observability
- End-to-end infrastructure visibility through Datadog
- Real-time metrics capture from cloud services
- Proactive monitoring dashboards
- Configured alerts for rapid issue identification and resolution
- Improved system reliability and reduced operational risk
Technology Stack
Cloud & Database:
- Microsoft Azure (cloud platform)
- SingleStore (MemSQL) for high-performance data storage
Data Processing & Streaming:
- Apache Airflow (orchestration)
- Apache Kafka (data streaming)
- Python, SQL, SAS (programming & analytics)
DevOps & Security:
- Azure DevOps (CI/CD)
- Azure Key Vault (secrets management)
- Datadog (monitoring & observability)
Impact
Successful Cloud Migration Program
- Large-scale migration program successfully initiated and executed
- Phased approach minimized business disruption
- Structured roadmap provided clarity across 18-24 month timeline
- Flexibility maintained to adapt to evolving business needs
Enhanced Technical Capabilities
- Improved scalability and performance of data workloads
- Cloud-native capabilities enable elastic growth
- Better support for peak operational demands
- Foundation for advanced analytics and machine learning
Strengthened Security & Governance
- Enterprise-grade security aligned with compliance requirements
- Automated deployment workflows reduce human error
- Role-based access controls improve data governance
- Audit trails and monitoring enhance operational visibility
Improved Collaboration & Efficiency
- Better collaboration between engineering, architecture, and business teams
- Week-by-week visibility into progress and dependencies
- Reduced manual intervention through automation
- Faster deployment cycles with CI/CD pipelines
Future-Ready Data Foundation
- Platform positioned for advanced analytics capabilities
- Scalable architecture supports long-term growth
- Modern cloud services enable innovation
- Reduced technical debt and maintenance overhead
Implementation Highlights
- Cloud-Native Pipelines: Designed and implemented data pipelines for multiple business domains
- Security Models: Built and optimized data and security models aligned with enterprise standards
- CI/CD Automation: Enabled continuous integration and deployment for data workflows and platform components
- Architecture Consulting: Provided solution design support to client architecture teams
- Operational Excellence: Initiated infrastructure-level monitoring and alerting for improved visibility
- Phased Delivery: Structured migration approach minimized risk while maintaining business continuity
Technical Highlights
- Enterprise Scale: Successfully migrated complex, mission-critical supply chain systems to cloud
- Zero Downtime: Phased approach maintained business continuity throughout transformation
- Modern Architecture: Cloud-native design supports both operational and analytical workloads
- Automation Excellence: CI/CD pipelines and infrastructure-as-code reduce manual effort
- Comprehensive Monitoring: End-to-end observability through Datadog integration
- Security First: Enterprise-grade security and governance from day one
Future Vision
The modernized platform positions the organization for continued growth and innovation:
- Advanced Analytics: Foundation ready for machine learning and AI capabilities
- Scalable Growth: Architecture supports 10x data volume increases
- Cloud-Native Innovation: Access to latest Azure services and capabilities
- Operational Efficiency: Continued automation and optimization opportunities
- Data-Driven Culture: Enhanced analytics capabilities support better business decisions
- Technology Leadership: Modern platform attracts top technical talent
Conclusion
This engagement demonstrates Edstem's expertise in delivering enterprise-scale cloud data transformations for complex, mission-critical environments. By combining deep technical knowledge with structured planning and close collaboration with PwC and client teams, Edstem enabled the organization to modernize confidently while maintaining supply chain operations. The resulting cloud-native platform provides a future-ready foundation for scalability, security, and advanced analytics capabilities that support long-term business value and competitive advantage.
