Data foundation
Sources, contracts, integration, quality and provenance designed as a controlled data flow.
DATA PLATFORM · FABRIC · WAREHOUSE
A data platform designed as a system, not a collection of services.
We design and modernise platforms built on Microsoft Fabric, Lakehouse and Warehouse — from integration and data quality to analytical models, security, observability and cost control.
Situations and scope of work
WHEN IT HELPS
Does this sound familiar?
- Data is copied between solutions without a clear source of truth or provenance controls.
- A new Fabric platform needs a target architecture, domain model and development standards.
- The existing warehouse does not scale, is costly to change or slows down new analytics.
- ETL/ELT processes are unstable, difficult to monitor or dependent on individual knowledge.
- The team needs a shared data layer for Power BI, advanced analytics and future AI use cases.
SCOPE OF WORK
What the engagement may include.
- 01
Fabric, OneLake, Lakehouse or Warehouse architecture matched to use cases and team capabilities.
- 02
Batch and incremental integration, ETL/ELT flows and source-change handling.
- 03
Data quality controls, testing, error handling and pipeline monitoring.
- 04
Dimensional models, data marts, semantic layers and clear data definitions.
- 05
Security, domain boundaries, access, retention and regulatory requirements.
- 06
Development, test and production environments, Git and deployment automation.
- 07
Performance, Fabric capacity, operational monitoring and cost control.
SOLUTION COMPONENTS
A platform ready for day-to-day operations.
The design covers more than data movement. It includes the rules for operating the platform, releasing changes and responding to issues.
Analytical layer
Dimensional models, warehouses, lakehouses and semantics prepared for reporting and data products.
Platform operations
Security, DevOps, monitoring, performance and cost built into the architecture from the start.
OUTCOME
What remains after the engagement.
- Known provenance, meaning, ownership and purpose for critical data.
- A consistent foundation for Power BI, reporting and future data products.
- Fewer manual dependencies and faster detection of failures and delays.
- Architecture and documentation that the client team can operate and evolve.
NEXT STEP
Define the right scope.
Describe the current state and expected outcome. The first step may be an assessment, an architecture review or a focused implementation.