Discovery and alignment
Confirm business objectives, target users, workloads, constraints and decision rights.
Primary output: agreed scope and success measures
Modern Data Platforms Service
Build and operate a governed Microsoft Fabric data platform
DataConsultant helps data, technology and business teams assess, design, implement and improve Microsoft Fabric. The service connects platform architecture, OneLake, ingestion, engineering, warehousing, real-time analytics, Power BI, governance and operating controls so organisations can move from fragmented analytics estates to a supportable, measurable platform.
- Assessment-led architecture and capacity planning
- Security, governance and lifecycle controls included
- Migration, implementation and optimisation support
- Knowledge transfer and operating-model documentation
Microsoft Fabric delivery architectureIllustrative target state
Business applications
Cloud and on-premises data
Events and APIs
OneLake foundationLakehouse, warehouse and shared governed data products
Data FactoryPipelines, Dataflow Gen2, copy and orchestration
Data EngineeringSpark, notebooks and Delta-based transformation
Data WarehouseSQL analytics, modelling and governed serving
Real-Time IntelligenceEventstreams, Eventhouse and KQL analytics
Power BISemantic models, Direct Lake and reports
GovernanceCatalog, lineage, labels, permissions and audit
Reusable data products
Trusted decision support
Controlled platform operations
Direct answer
What is a Microsoft Fabric service?
A Microsoft Fabric service is a structured combination of advisory, architecture, engineering, migration, governance and operational support for Microsoft’s unified analytics platform. It can cover Data Factory, OneLake, lakehouse, warehouse, Real-Time Intelligence, data science and Power BI, together with tenant administration, security, lifecycle management, capacity and adoption.
The objective is not simply to deploy Fabric features. It is to create a platform that supports defined business use cases, reliable data products, appropriate controls, predictable cost and clear ownership.
- Plan: establish business outcomes, workload scope and platform principles.
- Build: implement ingestion, storage, transformation, serving and analytics.
- Govern: define access, lineage, classification, workspace and release controls.
- Operate: monitor capacity, quality, reliability, adoption and service health.
Suitability
When Microsoft Fabric is a practical fit
The platform is most useful when technical consolidation supports a clear operating, governance or analytical objective.
Good-fit situations
- Power BI is strategic and teams need a more integrated data platform.
- Data engineering, warehousing and BI are fragmented across separate tools.
- The organisation wants lakehouse and warehouse patterns on a shared data foundation.
- Teams need governed self-service analytics with central guardrails.
- Near-real-time operational analytics is becoming important.
- Azure, Microsoft 365 and Microsoft Entra are already core technologies.
Cases requiring careful evaluation
- Current workloads depend heavily on features not supported or not mature in Fabric.
- Data residency, network isolation or regulatory controls require specialist review.
- Existing platforms already meet business needs at acceptable cost and risk.
- Workload peaks, concurrency or data volumes have not been measured.
- There is no accountable platform owner or operating support model.
- A rushed migration would create more technical debt than value.
Service scope
Microsoft Fabric consulting and implementation capabilities
Scope can be modular or end to end, depending on platform maturity, urgency and internal capability.
Strategy and readiness
Clarify priority use cases, workloads, stakeholders, constraints, target outcomes and adoption dependencies before platform decisions are locked in.
Architecture and foundation
Define tenant, capacity, domain, workspace, OneLake, lakehouse, warehouse, integration, semantic and network patterns aligned to security and support needs.
Data integration and engineering
Build repeatable ingestion and transformation using pipelines, Dataflow Gen2, notebooks, Spark jobs, mirroring, shortcuts, APIs and event-driven patterns.
Warehouse, BI and semantic layer
Develop trusted analytical models and consumption layers using Fabric Warehouse, SQL endpoints, semantic models, Direct Lake and Power BI.
Governance, security and DevOps
Implement practical controls for workspace access, data access, classification, lineage, deployment, source control, monitoring and operational accountability.
Optimisation and managed support
Improve capacity efficiency, job reliability, model performance, data freshness, incident handling and platform adoption through structured operations.
Outputs
Typical Microsoft Fabric deliverables
Deliverables are selected during scoping and should have named owners, review criteria and acceptance conditions.
| Workstream | Typical deliverables | Decision supported | Client participation |
|---|---|---|---|
| Assessment | Current-state inventory, readiness findings, dependency map and risk register | Whether, where and how to adopt Fabric | Stakeholder interviews, evidence and system access |
| Architecture | Target architecture, workspace and domain model, OneLake design and integration patterns | How the platform should be structured | Architecture, security and operations review |
| Engineering | Pipelines, notebooks, dataflows, lakehouse tables, warehouse models and test evidence | How data is acquired, transformed and served | Source access, rules, data owners and acceptance testing |
| Analytics | Semantic models, measures, Direct Lake configuration, reports and performance findings | How users consume trusted information | Business definitions, user testing and adoption input |
| Governance | Access matrix, workspace standards, naming rules, lineage, label and release controls | How risk and accountability are managed | Security, privacy, compliance and data-owner approval |
| Operations | Runbooks, monitoring model, capacity dashboard, incident routes, service KPIs and handover | How Fabric is supported after launch | Operations ownership and support readiness |
Delivery process
How DataConsultant delivers Microsoft Fabric work
The sequence is adapted to the assignment; no fixed duration is assumed before discovery.
Current-state assessment
Review sources, pipelines, models, reports, security, capacity and operating practices.
Primary output: findings and dependency registerTarget-state design
Define architecture, domains, workspaces, data products, controls and migration patterns.
Primary output: solution design and prioritised backlogFoundation and build
Configure platform controls and implement ingestion, transformation, serving and analytics.
Primary output: working Fabric componentsValidation and release
Test data, performance, security, recoverability, deployment and user acceptance.
Primary output: test evidence and release decisionTransition and improvement
Complete documentation, knowledge transfer, monitoring and optimisation planning.
Primary output: operational handover and improvement planArchitecture
A practical Microsoft Fabric platform pattern
The final pattern depends on sources, latency, data domains, consumption needs, skills, controls and cost.
Source and movement
- Operational databases and SaaS applications
- Files, APIs and event streams
- Copy, mirroring, shortcuts and gateways
- Batch and near-real-time orchestration
OneLake and processing
- Domain-aligned lakehouses and warehouses
- Delta tables and curated data products
- Spark, SQL and low-code transformation
- Quality, lineage, ownership and access controls
Consumption and operations
- Semantic models and Direct Lake
- Power BI and embedded analytics
- Real-time dashboards and alerts
- Capacity, reliability and adoption monitoring
Governance and assurance
Controls that should be designed with the platform
Governance is most effective when embedded into architecture, delivery and operations rather than added after deployment.
01
Identity and access
Use Microsoft Entra groups, workspace roles, item permissions, data-access patterns and privileged administration with clear segregation of duties.
02
Data protection
Define classification, sensitivity labels, row or object-level controls, sharing rules, retention and residency requirements with authorised reviewers.
03
Catalog and lineage
Establish ownership, descriptions, endorsement, lineage review and discoverability across OneLake Catalog and relevant Microsoft Purview capabilities.
04
Delivery controls
Apply source control, environment separation, deployment pipelines, testing, release approvals and traceable change management.
05
Platform operations
Monitor capacity, failures, refreshes, Spark and warehouse usage, data freshness, incidents, support ownership and service continuity.
06
Evidence and compliance
Maintain architecture decisions, control evidence, risk acceptance, processing records and audit trails appropriate to sector and jurisdiction.
Important limitation: DataConsultant can support technical and governance design, but legal interpretation, regulatory sign-off, cybersecurity certification and formal audit opinions require appropriately authorised specialists.
Engagement models
Ways to engage for Microsoft Fabric support
| Model | Best suited to | Typical scope | Commercial approach |
|---|---|---|---|
| Assessment | Organisations evaluating Fabric or identifying migration risk | Readiness, architecture review, capacity considerations and roadmap | Defined project scope |
| Implementation project | Teams with approved use cases and platform sponsorship | Foundation, engineering, warehouse, BI, governance and handover | Milestone or time-and-materials structure |
| Migration workstream | Existing Power BI, Synapse, ADF or lake environments | Inventory, compatibility, remediation, testing, cutover and stabilisation | Phased waves based on asset complexity |
| Specialist augmentation | Internal teams needing specific architecture or engineering capability | Named roles embedded into client delivery governance | Dedicated or fractional specialists |
| Managed support | Teams needing continuing platform operations and improvement | Monitoring, incidents, optimisation, releases, quality and reporting | Recurring service with agreed scope and service levels |
| Training and enablement | Platform owners, engineers, analysts and governance teams | Role-based learning, standards, labs, coaching and documentation | Workshop, cohort or ongoing enablement |
Measurement
Microsoft Fabric outcomes and KPIs
Targets should be baseline-based and attributable to the agreed scope rather than assumed platform benefits.
Data delivery lead timeTime from approved requirement to production data product
Pipeline reliabilitySuccessful runs, failure rate and mean time to recovery
Data freshnessPercentage of critical datasets meeting agreed latency
Quality performanceCritical rule pass rates and issue resolution cycle time
Capacity efficiencyUtilisation, throttling, peak demand and workload distribution
ReuseConsumption of governed data products and shared semantic models
AdoptionActive users, report usage and training completion
Control adherenceWorkspace, access, release and documentation compliance
Pricing and dependencies
What affects Microsoft Fabric service cost and timing?
A credible estimate requires discovery because software capacity and consulting effort are influenced by different factors.
Scope and workload mix
Number of domains, sources, pipelines, lakehouses, warehouses, semantic models, reports and real-time use cases.
Migration complexity
Asset inventory, compatibility, technical debt, refactoring, data reconciliation, parallel run and cutover requirements.
Security and regulation
Network design, identity, data residency, privacy, audit evidence, segregation and third-party assurance needs.
Capacity and operations
Concurrency, refresh windows, Spark and SQL demand, Power BI consumption, peaks, environments, resilience and support coverage.
Capacity planning: Fabric capacity is purchased separately from consulting services. Capacity should be tested and monitored against representative workloads before long-term sizing decisions are made.
Provider evaluation
What to assess in a Microsoft Fabric service provider
End-to-end platform understanding
Look for capability across engineering, warehouse, Power BI, governance, security, DevOps, capacity and operations rather than a single workload only.
Evidence-conscious delivery
Require documented assumptions, architecture decisions, test evidence, risk logs, acceptance criteria and transparent limitations.
Operating-model fit
Confirm how the provider will work with internal teams, Microsoft partners, security, compliance, procurement and support functions.
Frequently asked questions
Microsoft Fabric service FAQs
Practical answers for business, data, technology, procurement and governance teams.
What is Microsoft Fabric?
Microsoft Fabric is a unified SaaS analytics platform that brings together data integration, engineering, warehousing, real-time analytics, data science and Power BI around OneLake. Organisations still need deliberate architecture, governance, capacity management and operating controls to use it effectively.
What is included in DataConsultant's Microsoft Fabric service?
Scope can include readiness assessment, strategy, architecture, capacity planning, tenant and workspace design, OneLake, Data Factory, lakehouse, warehouse, Real-Time Intelligence, semantic models, Power BI, migration, security, governance, DevOps, testing, optimisation, training and managed support.
Can you assess whether Microsoft Fabric is right for our organisation?
Yes. An assessment can compare business objectives, existing platforms, workload requirements, skills, governance, security, cost and migration dependencies. The outcome may recommend full adoption, targeted use, phased adoption, coexistence or deferral.
Can existing Power BI solutions be migrated to Fabric?
Yes, but the required work varies. Migration may involve workspace redesign, semantic-model changes, Direct Lake evaluation, gateway review, refresh redesign, security validation, deployment controls, report testing and licensing analysis.
Can Azure Data Factory and Synapse workloads be migrated?
Many workloads can be moved or redesigned using Fabric Data Factory, notebooks, lakehouses and warehouses. Compatibility, connectors, orchestration logic, Spark code, SQL features, networking, monitoring and operational dependencies must be assessed asset by asset.
How do OneLake shortcuts affect architecture?
Shortcuts can provide governed references to internal or external storage without creating another full data copy. Their suitability depends on source support, permissions, performance, schema behaviour, ownership, availability and data-residency requirements.
How is Microsoft Fabric capacity selected?
Capacity should be based on measured workload demand, concurrency, refresh patterns, Spark and warehouse activity, Power BI consumption, peak periods, growth and resilience. Trial or pilot workloads and capacity metrics should inform sizing rather than estimates alone.
How long does a Microsoft Fabric implementation take?
There is no responsible fixed answer before discovery. Timing depends on scope, source access, data quality, architecture approvals, security requirements, migration volume, testing, stakeholder availability, release governance and internal capability.
How is Microsoft Fabric consulting priced?
Consulting price is influenced by assessment depth, number of workloads and domains, technical complexity, migration effort, governance requirements, deliverables, client participation, location, support coverage and engagement model. Fabric software and capacity charges are separate.
Does Microsoft Fabric replace every existing data platform?
Not necessarily. Fabric can consolidate many analytics capabilities, but some workloads may remain on existing platforms because of performance, functionality, regulation, contracts, skills or economics. Coexistence can be a valid target architecture.
What security controls are relevant?
Controls can include tenant settings, Microsoft Entra groups, workspace roles, item permissions, OneLake access, row and object-level security, sensitivity labels, private connectivity options, secrets, audit logs, sharing controls and release approvals.
How are data quality and lineage handled?
Data quality rules, ownership, issue workflows, reconciliation and monitoring should be designed into pipelines and data products. Lineage, catalog metadata, descriptions, endorsement and governance responsibilities should be maintained across Fabric and connected governance tooling.
Can DataConsultant provide managed Microsoft Fabric support?
Yes. Managed scope can include monitoring, incident coordination, pipeline and refresh support, capacity review, release assurance, data-quality reporting, platform administration, optimisation and improvement planning. Service boundaries and response expectations must be agreed.
What does the client need to provide?
Useful inputs include business priorities, source and asset inventories, architecture diagrams, access, data definitions, security policies, licensing information, usage evidence, risk findings, test users and accountable stakeholders. Missing evidence is documented as a delivery limitation.
What are common Microsoft Fabric implementation risks?
Common risks include adopting without a use-case roadmap, under-sizing or over-sizing capacity, weak workspace governance, uncontrolled self-service growth, insufficient data ownership, migration assumptions, poor testing, unclear support ownership and treating Fabric as a tool deployment rather than an operating platform.
Discuss your Microsoft Fabric requirements
Share your current platforms, priority workloads, governance constraints and expected outcomes for a practical view of assessment, implementation or optimisation options.