Microsoft Azure Databricks Design and Implementation
All for One Group SE
Design and implement a secure, scalable Microsoft Azure Databricks Data Platform including ingestion, medallion data model, semantic models, governance, DevOps, and business‑ready Power BI reporting
Design and implement a secure, scalable Microsoft Azure Databricks Data Platform including ingestion, medallion data model, semantic models, governance, DevOps, and business‑ready Power BI reporting
This offer establishes a complete Microsoft Azure Databricks–based analytics data platform that enables organizations to integrate SAP and non‑SAP data, standardize analytics delivery, and operate with enterprise‑grade governance and security. The service covers architecture, implementation, and operational handover. We design and build a medallion‑based data platform in Azure Databricks (Bronze, Silver, Gold) on Azure Data Lake Storage, implement robust ingestion pipelines, develop curated data products, and provide semantic models for consistent reporting in Power BI and Excel.
What is included
- Target architecture and implementation blueprint for Microsoft Azure Databricks
- Source integration patterns for SAP and non‑SAP systems
- Data ingestion pipelines with delta loading, error handling, monitoring, and restart logic
- Lakehouse data modeling across Bronze, Silver, and Gold layers
- Semantic modeling standards (star schema, KPI definitions, naming conventions, performance rules)
- Power BI–ready serving layer with reusable measures and governed datasets
- Security and governance setup including role‑based access control (RBAC) and data governance processes
- DevOps and ALM setup (Git integration, CI/CD, release governance, traceability)
- Monitoring and operations setup, including incident and change process guidance
- Knowledge transfer, documentation, and transition to customer operations
Business outcomes
- Single governed analytics platform based on Microsoft Azure Databricks
- Faster onboarding of new data sources and analytics use cases
- Improved data quality, traceability, and audit readiness
- Standardized KPI logic and consistent enterprise reporting
- Reduced operational risk through DevOps automation, monitoring, and clear ownership