Most organizations struggle to extract, integrate, and activate SAP data outside its native environment. Combining SAP and non-SAP data while retaining business meaning remains the single biggest data challenge enterprises face today.
Complex SAP data structures, custom configurations, and compliance requirements make it one of the hardest sources to bring into modern analytics platforms.
When SAP and non-SAP data live in separate worlds, you can't build the unified enterprise view that AI and advanced analytics require.
Building SAP-to-Databricks data pipelines typically takes 3–4 months. That's too slow for businesses needing AI-ready data now.
Your BW system holds decades of transformation rules, hierarchies, and reporting logic. Migrating to Databricks without preserving this IP means starting from scratch — or losing critical business intelligence.
AI requires clean, governed and business context-ready enterprise data. Without a structured approach to SAP data activation, AI projects fail at the data layer before they ever reach the model layer.
- Enterprise data platform assessment
- Lakehouse architecture design
- SAP and non-SAP data integration strategy
- Cloud data platform roadmap
- Unity Catalog adoption planning
- Data mesh & domain architecture
- Data Lakehouse architecture build
- Scalable Delta pipelines and data engineering
- RBAC and data governance frameworks
- Delta Sharing for cross-team data access
- Performance and cost optimization
- Medallion architecture (Bronze / Silver / Gold)
- SAP BW to Databricks migration
- SAP data ingestion pipelines
- Integration with SAP Datasphere & BDC
- Enterprise semantic layer modernization
- SAP business object metadata migration
- Predictive analytics models on Databricks
- ML pipelines with MLflow & Model Registry
- AI-driven decision automation
- Agentic AI use cases on enterprise data
- Feature Store for reusable ML features
- Vector Search for AI and RAG use cases
Decades of hands-on experience across ECC, S/4HANA, BW, BW/4HANA, and SAP Datasphere. This isn't generic ETL — it's precision data engineering built on deep domain knowledge of SAP data models, business logic, and enterprise semantics.
We cover the full data lifecycle within Databricks: data engineering, pipeline automation, Lakehouse architecture design, SAP + non-SAP data unification, ML model development, AI enablement, and analytics layer design.
Our IRIS platform integrates seamlessly with Databricks to accelerate AI use cases — from demand forecasting and supply chain optimization to intelligent document processing and predictive maintenance.
We design hybrid architectures that integrate SAP data platforms with Databricks and hyperscalers, creating a unified data foundation. SAP Systems → SAP Datasphere/BDC → Databricks Lakehouse → BI, AI, and Advanced Analytics.
Our proprietary framework eliminates the 3–4 month wait typically associated with building SAP-to-Databricks data pipelines. Battle-tested at large enterprises running complex SAP landscapes. Includes data masking and compliance accelerators to keep your SAP data secure from day one.
vs. 3–4 months typical
Faster onboarding
Masking & compliance built in
Deep domain expertise
CeleRIX, our AI-powered migration framework, automatically maps BW data flows, extracts transformation logic, and generates equivalent Databricks artifacts — preserving the decades of business rules embedded in your BW system. Includes a complimentary 2–3 week assessment to map your BW landscape complexity.
Powered code generation
Free BW assessment
Business logic preserved
Enterprise success stories