Migrating Global Logistics Data to Azure with Zero Downtime
Modernizing complex supply chain data ecosystems with Azure, SingleStore, Airflow, and Kafka for scalability, security, and analytics readiness.
Migrating Global Logistics Data to Azure with Zero Downtime
Modernizing complex supply chain data ecosystems with Azure, SingleStore, Airflow, and Kafka for scalability, security, and analytics readiness.
Modernizing Complex Supply Chain Data Environments
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.
IMPACT
High
Operational Risk
Phased Migration & Cloud Modernization Approach
Edstem employed a phased, agile delivery methodology with close collaboration with client and PwC teams: Assessment & Planning: System categorization and migration roadmap Platform Design: Cloud-native architecture on Azure Data Engineering: Pipeline migration and orchestration DevOps Implementation: CI/CD automation and secure deployments Monitoring Setup: Datadog-based observability Phased Migration: Domain-by-domain execution with risk mitigation Each system was assessed based on business criticality, data size, dependencies, and migration risk to build a flexible 18-24 month roadmap.
Technology Stack
Cloud-Native Data Platform & Orchestration
Edstem delivered a comprehensive cloud-first solution: Cloud-First Data Platform Microsoft Azure cloud native architecture Elastic scalability for growing data volumes Improved performance, reliability, and security Support for operational and analytical workloads Integration with modern cloud data services Data Engineering & Orchestration Robust pipelines for batch and scheduled processing SQL based transformations, Python driven workflows Apache Airflow for workflow orchestration Consistency, reliability, and visibility across data flows DevOps & Security Enablement CI/CD pipelines via Azure DevOps Version controlled infrastructure and deployment workflows Azure Key Vault for secrets management Role based access models aligned with enterprise security Governance and compliance alignment Infrastructure Monitoring & Observability Datadog for end to end visibility and proactive dashboards Real time metrics capture and alerts Rapid issue identification and resolution Improved system reliability and reduced operational risk Technology Stack Cloud & Database: Microsoft Azure, SingleStore Data Processing & Streaming: Apache Airflow, Apache Kafka, Python, SQL, SAS DevOps & Security: Azure DevOps, Azure Key Vault, Datadog
Cloud-Native Platform
Scalable, reliable, and secure architecture supporting operational and analytical workloads
Data Engineering & Orchestration
Robust pipelines, Airflow workflows, and Python/SQL transformations for consistent and reliable data processing
DevOps & Observability
CI/CD automation, secure infrastructure, and Datadog monitoring for operational reliability
- Automation & Security
- End-to-End Visibility
Cloud-Native Architecture
Scalable and secure platform on Azure
Data Orchestration
Airflow, Python, SQL pipelines
Security & Governance
Role-based access and Key Vault integration
Monitoring & Observability
Datadog dashboards and alerts
Impact & Outcomes
1. Successful Cloud Migration: Phased execution minimized disruption 2. Enhanced Technical Capabilities: Elastic, scalable data pipelines and workloads 3. Strengthened Security & Governance: Compliance-aligned and automated workflows 4. Improved Collaboration & Efficiency: Cross-team visibility and faster deployments 5. Future-Ready Data Foundation: Ready for advanced analytics, AI/ML, and long-term scalability
Before
- Legacy infrastructure with limited scalability
- Complex interconnected pipelines across domains
- High risk of operational disruption
- Manual processes and limited governance
After
- Cloud-native platform on Azure with SingleStore
- Elastic, reliable, and secure data pipelines
- Automated CI/CD workflows and DevOps enablement
- Full monitoring and observability with Datadog
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