- What We Do
TRANSFORM
- Who We Serve
- Technologies
SAP
Data & Analytics
Technologies — Data & Analytics
Fabric &
Datasphere
The data engineering layer that everything else depends on. Before Power BI, before AI, before any analytics programme — you need clean, governed, connected data. Microsoft Fabric unifies your D365 and third-party sources. SAP Datasphere governs your S/4HANA and SuccessFactors data at the source. We build both, connected, as one foundation.
"Data engineering is the unglamorous work that makes everything else possible. We build it right the first time — so you are not rebuilding it when your AI programme needs a foundation it can trust."
Spaces
Governed Datasphere spaces, one per business domain
OneLake
One logical copy of data, no duplication between Fabric and Power BI
Weekly
Automated ML model refresh — zero manual intervention
Purview
Data lineage and governance across Fabric and Datasphere
What We Do
Enterprise data engineering — from ingestion to insight
Microsoft Fabric
Microsoft's unified analytics SaaS platform — OneLake as a single logical data lake, Direct Lake for Power BI without imports, Dataflow Gen2 for low-code ingestion. Our go-to for unifying D365, third-party, and Datasphere-published data on the Microsoft side.
SAP Datasphere
SAP's own data fabric — Spaces for governed domain ownership, Replication Flows and federation for S/4HANA, BW/4HANA, and SuccessFactors data, Business Builder for semantic modelling. Data stays governed at the SAP layer instead of being extracted and duplicated elsewhere.
SAP & ERP Data Ingestion
Datasphere Replication Flows and federation from S/4HANA, SuccessFactors, and BW/4HANA — real-time federation where you need it live, replication where you need it fast, with automated reconciliation to verify completeness.
OneLake Data Architecture
Medallion-layered data in Fabric's OneLake — raw ingestion, cleansed and conformed data, business-ready aggregates — with data quality rules enforced at every layer and full lineage tracked in Purview, connected to what Datasphere publishes from SAP.
Data Quality & Governance
Automated data quality checks, anomaly detection, and governance policies enforced at ingestion — with Microsoft Purview for lineage, classification, and the audit trail that regulated industries require.
Predictive ML on Azure ML
Azure ML pipelines fed by Fabric and Datasphere data — demand forecasting, attrition prediction, and risk models that retrain weekly without manual intervention, surfaced directly in Power BI.
Semantic Layer & Power BI Integration
Certified Power BI semantic models built on Fabric's OneLake and Datasphere's business layer — governed datasets every team uses as their single source of truth, with row-level security and sensitivity labels applied.
Real-time & Streaming Analytics
Fabric Real-Time Intelligence and Eventstreams for near-real-time data — IoT sensor data, transactional event streams, and operational telemetry feeding dashboards and alerts that respond in seconds, not hours.
Solution Accelerators
Pre-built. Battle-tested. Ready to deploy.
SAP Ingestion Kit
S/4HANA & SuccessFactors to Datasphere Replication Flow Templates
Pre-built Replication Flows for the most common SAP extraction patterns, federation where real-time access is needed, with automated reconciliation.
- S/4HANA FI, MM, SD, PP, and HR extraction pipelines
- SuccessFactors OData API extraction with pagination handling
- Incremental delta extraction using change pointers and timestamps
- Automated reconciliation — record count and sum validation
Enterprise Data Foundation Blueprint
Enterprise Lakehouse Architecture Template
infrastructure-as-code deploying a governed Fabric workspace and Datasphere space together — OneLake, Purview integration, connected from day one.
- Terraform templates for Fabric workspace and Datasphere space
- Bronze-silver-gold layer structure with data quality enforcement
- Purview data catalog and lineage configuration
- Azure Monitor alerts and pipeline failure notifications
ML Pipeline Template
Demand Forecasting & Attrition Prediction Templates
AzureML-managed model pipelines for the two most common enterprise ML use cases — demand forecasting on S/4HANA SD data and attrition prediction on SuccessFactors HCM data.
- Feature engineering notebooks for S/4HANA and SF data
- Model training, validation, and selection pipeline
- Azure ML model registry with automated deployment to serving
- Power BI integration — prediction scores as a data source
Case Studies
Delivered. Not just proposed.
Testimonials
Client Testimonials
We had a previous data warehouse that was built in six months and abandoned in twelve because nobody trusted the numbers. DWC built the Fabric and Datasphere foundation correctly — data quality rules at every layer, Purview lineage on everything. It has been running for eight months without a data issue. The difference is the foundation.
CTO
Manufacturing Group · Fabric & Datasphere
The attrition model running on Datasphere and Azure ML has changed our retention strategy. The model identifies risk 60 days out with over 80% accuracy. Our HRBPs now have time to intervene while it still matters. DWC built the pipeline so it retrains automatically — our team does not need to touch it.
CHRO
ITeS Firm · Datasphere ML
The demand forecasting model paid for the entire data engineering programme within two quarters. We had always known our reorder points were wrong — we just could not calculate the right ones without months of analyst effort. Now the model does it weekly. Inventory costs are down and our procurement team is buying on data.
Head of Supply Chain
O&G Services · Fabric + Forecasting
Start with a data architecture assessment.
We will map your current data sources, identify the gaps in
your foundation, and map what belongs in Fabric, what stays governed in Datasphere, and how the two connect for your specific estate.