Data foundation
Sources, contracts, integration, quality and provenance designed as a controlled data flow.
DATA PLATFORM · FABRIC · WAREHOUSE
Microsoft Fabric and data warehouse implementation
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.
FAQ
Questions before the project
Where should a Microsoft Fabric implementation start?
With one business use case, its sources and data acceptance criteria. The agreed scope may include architecture, a working data flow, an analytical model, security, and the documentation and monitoring needed to operate the solution.
What affects the cost and duration of a Fabric implementation?
Source count and quality, historical scope, loading frequency, modelling, security and operational requirements. The existing environment and team capabilities also matter. We agree the price, phases and schedule for a specific scope.
Must we replace our ERP and existing data warehouse?
That is not assumed. The service described builds an analytical layer, not a new ERP. We can integrate the required data and modernise selected flows; migration, operations and handover decisions follow the agreed architecture.
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.