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GxP-Compliant Posit Stack Modernization with Kubernetes Integration
  1. case
  2. GxP-Compliant Posit Stack Modernization with Kubernetes Integration

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GxP-Compliant Posit Stack Modernization with Kubernetes Integration

appsilon.com
Medical
Information technology

Regulatory Compliance and Infrastructure Modernization Challenges

Legacy Posit stack failed to meet updated GxP compliance requirements, posing regulatory risks. Inconsistent environments created technical debt, while lack of Kubernetes integration limited scalability. Data science teams lacked software development best practices, increasing support workload and operational inefficiencies.

About the Client

Global pharmaceutical company requiring regulatory-compliant data science infrastructure

Modernization Goals

  • Achieve full GxP compliance for Posit stack
  • Consolidate three disparate environments into standardized architecture
  • Implement Kubernetes integration for scalable resource management
  • Reduce technical debt through migration rather than incremental upgrades
  • Empower teams through knowledge transfer and automation

Core System Requirements

  • GxP-compliant Posit system migration with switchover strategy
  • Kubernetes-native architecture integration
  • Ansible-automated installation/configuration
  • Standardized multi-environment framework
  • Comprehensive qualification documentation

Technology Stack

Ansible
Kubernetes
Docker
Posit
Git

System Integrations

  • Internal Kubernetes cluster
  • Version control system
  • GxP audit framework

Operational Requirements

  • High availability and scalability via Kubernetes
  • Full audit trail and change management capabilities
  • Regulatory compliance documentation traceability
  • Disaster recovery and rollback mechanisms
  • Security-hardened container orchestration

Business Impact

Eliminated regulatory risks through GxP compliance, reduced support workload by 40% via automation, and enabled 300% greater infrastructure scalability. Standardized environments improved operational efficiency while maintaining zero workflow disruption for data science teams.

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