Design and build a Fabric lakehouse foundation - structured storage, engineering standards, curated layers, and operational controls so analytics and BI have a reliable source of truth.
A lakehouse approach can accelerate analytics by combining flexible data storage with engineering and BI consumption patterns in one platform. In Microsoft Fabric, lakehouse capabilities allow teams to ingest, store, transform, and serve data products quickly - but only when the lakehouse is designed with structure and governance. When organisations build lakehouses ad-hoc, they often end up with inconsistent file layouts, unclear dataset ownership, overlapping transformations, and unreliable refresh patterns. Over time, data quality and trust degrade and teams spend more time repairing pipelines and definitions than delivering value.
LW IT Solutions delivers Fabric Lakehouse Design & Build as a structured service to create a governed lakehouse foundation that supports repeatable data engineering and reliable consumption. We define how your organisation will land data, standardise schemas, implement curated layers, and document business rules so the lakehouse becomes a dependable source for downstream reporting and warehousing. You receive standards, an implemented pilot (or first subject area), and an operating model that enables scale - so future data products follow the same patterns and remain supportable as adoption increases.
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Service Overview
Highlights
- Microsoft Fabric lakehouse design aligned to OneLake
- Structured storage layout using medallion patterns
- Standardised ingestion and transformation approaches
- Curated datasets prepared for analytics and BI consumption
- Operational focus covering monitoring, refresh, and supportability
Business Benefits
- Create a reliable source of truth for analytics and BI within Microsoft Fabric
- Reduce rework by standardising ingestion, schemas, and transformation patterns
- Improve trust in data through curated layers with documented business rules
- Increase delivery speed for new data products using repeatable lakehouse patterns
- Support stable operations with defined monitoring, refresh, and support processes
Typical use cases
- Organisations establishing a Fabric lakehouse as a data platform foundation
- Teams replacing ad-hoc data pipelines with structured engineering patterns
- Analytics programmes needing trusted, reusable datasets for reporting
- Enterprises scaling Fabric across multiple data domains
- Data teams requiring a supportable lakehouse operating model
Objectives & deliverables
What Success Looks Like
- Establish a structured lakehouse foundation aligned to your data domains and use cases
- Standardise ingestion, schema handling, and transformation patterns to reduce rework
- Deliver curated layers that improve trust and reuse across reporting and analytics
- Implement operational controls for refresh reliability, monitoring, and supportability
- Create a repeatable pattern your team can scale across additional data products
What You Get
- Lakehouse architecture and standards pack (layout, naming, layering approach, schema handling)
- Implemented lakehouse pilot or first subject area within agreed scope
- Curated datasets with documented business rules and validation outcomes
- Operational handover pack: monitoring guidance, runbooks, and support model recommendations
- Roadmap/backlog for additional sources, domains, and lakehouse maturity improvements
How It Works
- Discovery - confirm data domains, priority use cases, and consumption patterns
- Architecture design - define lakehouse layout, medallion layers, and standards
- Build foundations - configure OneLake structure, schemas, and core engineering patterns
- Implement pilot - ingest and curate the first subject area within agreed scope
- Validation - test data quality, refresh behaviour, and downstream consumption
- Handover - document standards, runbooks, and a roadmap for scaling the lakehouse
Engagement Options
- Design - define lakehouse architecture, standards, and operating model
- Pilot - build a first Fabric lakehouse subject area to prove patterns
- Build - implement multiple curated layers and production-ready pipelines
- Scale - extend the lakehouse across additional domains with governance
Common Bundles
Customers who use this service often bundle with these services
Fabric Data Factory (ETL/ELT) Pipelines
Design and build Microsoft Fabric Data Factory pipelines with repeatable patterns, reliable scheduling, monitoring, and error handling across data sources.
Fabric Governance, Security & Cost Control
Establish Microsoft Fabric governance with workspace strategy, role based access, auditing, environment separation, and cost controls for predictable operation.
Fabric Data Warehouse Implementation
Design and implement Microsoft Fabric data warehouses with clear models, controlled access, and predictable performance for trusted enterprise reporting.
Power BI Dashboard Design & Integration
Power BI dashboard design and integration delivering trusted executive and operational reporting through strong data modelling, security and reliable refresh.
Frequently Asked Questions
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