The company operates a global development and production network with numerous data domains – from R&D to procurement to maintenance. Until now, this data has been isolated, with access sometimes manual and non-standardized. The goal was to create a unified platform that focuses on consistent standards, rapid evaluability, and AI readiness.
Challenge
A leading European technology company with a global production and development network faced the challenge of scaling its data-centric strategy: Numerous data domains – from Engineering to Supply Chain – were organized in separate silos. The Data Platform initiative aimed to create the foundation for unified standards, automated pipelines, and AI readiness.
Enterprise Data Platform on Microsoft Azure – together with DIVINT
DIVINT has been commissioned as the engineering partner to implement data pipelines, governance structures, and architectural components in an enterprise-wide cloud platform.
Ingestion Layer: Building scalable pipelines with Azure Data Factory, Synapse & Spark
Data Lake Architecture: Structuring by Bronze, Silver, Gold – per data domain
Standardization: Development of YAML-based pipeline templates & metadata catalog
Security & Compliance: Role-Based Access, Purview, GDPR-compliant logging infrastructure
CI/CD: GitOps approach for infrastructure & code deployment (Terraform + ADO)
Core Focus Areas in the Project
Data Engineering Frameworks
DIVINT developed a central framework for rule-based pipelines, which is now used across the company for all data domains – focusing on reusability, logging, alerting & schema validation.
Multi-Tenant Architecture
Isolated environments were built on a shared infrastructure for different departments (e.g. R&D, Procurement, MRO) – with a central access concept & decentralized responsibility.
Automated Metadata Management
Data flows, quality scores, and lineage are automatically passed to Purview – the basis for self-service, auditability, and strategic data governance.
AI-Readiness & ML-Enablement
The platform is designed for data science & ML workloads – with integrated access to feature stores, datasets, and version history (Delta Lake + MLflow).
Results and Business Impact
KPI | Result |
|---|---|
Monthly Data Flows | over 5,000 orchestrated pipelines |
Reusable Components | over 50 generic templates for ingestion & transformation |
Time-to-Data (from source to analysis) | Less than 24h (instead of at least 5 days before) |
Security Coverage | 100% role-based, multi-tenant |
AI/ML Projects | Platform-ready with Feature Store, DQ & Notebooks |
Next Steps
Expansion of the platform to additional works & international locations
Automated data contracts between departments
Establishment of a self-service data portal for citizen developers
Native connection of real-time data (IoT streams) via Azure Event Hub & Synapse Real-Time
Conclusion
Together with DIVINT, the technology corporation was able to establish a scalable, secure, and AI-capable data platform on Azure within a few months. The solution enables a unified data strategy – from engineering to operations – and shortens decision-making processes through consistent data models, self-service, and AI-supported applications.
