Microsoft Fabric Consulting Services
Most companies come to us with the same problem: data scattered across Azure services, Power BI reports nobody fully trusts, and a Fabric capacity bill nobody can explain. We fix that.
We’ve been building production data platforms for more than 30 years — long before Microsoft Fabric existed. When Fabric launched, we adopted it because our clients needed a unified analytics platform that brought data engineering, BI, and AI into a single environment. We’d already been doing that work on Snowflake for eight years, and on traditional data warehouses for decades before that.
We don’t recommend Fabric because it’s the only platform we know. We recommend it when it’s the right fit. And we tell you honestly when it isn’t.
Scale Unified Analytics With Our Microsoft Fabric Consulting
Official MS Azure Partner
Why Companies Hire a Microsoft Fabric Consultant
Most companies that come to us for Fabric consulting don’t have the internal team to build this themselves, and they’re not looking for a vendor whose incentives are tied to license sales.
- No data engineering team: Building OneLake architecture, Data Factory pipelines, and semantic models takes skills most mid-market teams haven't hired for.
- Focused on running the business, not the data stack: Time spent learning Fabric's capacity model is time not spent on the business itself.
- One partner across the entire data and AI stack: Not four vendors, each owning one layer and pointing at the others when something breaks.
- A roadmap before a build, not implementation as the first step: Companies need to know what to build first and what it costs before committing budget.
- Independent of Microsoft licensing revenue: We don't earn on the SKU we recommend. That's why we can tell you when Snowflake fits better, or when staying on your current Azure setup is the smarter move.
We’ve spent 8+ years building Snowflake data warehouses and over 30 years designing data architectures across every major platform. When we recommend Fabric, it’s because we’ve evaluated the alternatives (Snowflake, hybrid architectures, staying on existing Azure services) and determined it’s the best path forward.
Our Microsoft Fabric Consulting Services
Every Fabric engagement follows the same sequence: align on strategy before touching a platform, centralize data before modeling it, design the architecture before building it, and transform raw data into something analysts can actually use. The services below are organized in that order. Typical mid-market implementations run 8–12 weeks from kickoff to production; enterprise-scale migrations run 16–20 weeks.
Microsoft Fabric Strategy & Platform Assessment
What it is:
A strategy engagement that establishes whether Microsoft Fabric is the right platform for your organization before a single pipeline is written. It aligns data investments with business priorities and produces a phased roadmap grounded in your current systems, team capabilities, and workload requirements.
What it includes:
Data & systems inventory
Cataloging data sources, discovering gaps, and assessing data quality and team capabilities.
Platform assessment
Evaluating whether Fabric, Snowflake, a hybrid architecture, or your existing Azure services is the right path forward.
Use case mapping
Identifying high-impact business questions and the data needed to answer them.
Roadmap development
A phased plan that sequences investments so each one compounds on the last.
What it solves:
Teams that enable Fabric without a strategy end up with OneLake as a data swamp and a climbing Azure bill they can’t explain. If Fabric isn’t the right fit, we tell you before you’ve spent a dollar on implementation — we can say that because we’ve built production systems on both platforms.
Deliverable:
Assessment summary with a platform recommendation, architecture direction, and cost projection.
What it includes:
OneLake architecture design
Domain boundaries, workspace structure, and dimensional models that map to your organization’s access patterns and security requirements.
Data integration
Connecting CRMs, ERPs, SaaS tools, and operational databases into OneLake using Fabric Data Factory, with Fivetran for non-Microsoft sources.
OneLake shortcuts
OneLake shortcuts — virtually connecting to data in S3, GCS, or ADLS Gen2 without copying it; shortcut caching cuts cross-cloud egress costs on repeated reads.
Workspace governance
Ownership, access controls, and quality standards that scale with your business.
What it solves:
OneLake without dimensional modeling and domain governance becomes a data lake with a new name. Implementation done right produces a single source of truth, not another layer of sprawl.
Microsoft Fabric Implementation Services & OneLake Architecture
What it is:
End-to-end implementation that designs OneLake as a true governed source of truth — not a storage bucket with a Microsoft logo on it. This covers the architecture and integration work that brings scattered systems into a unified, well-structured platform.
Microsoft Fabric Migration Assessment
What it is:
Structured migration from a fragmented Azure estate — or from another platform — into Fabric, with a quantified assessment before anything moves.
What it includes:
Azure-to-Fabric consolidation
Auditing your existing Azure stack (Synapse Analytics, Azure Data Factory, Azure SQL, Power BI Premium), mapping workloads to Fabric equivalents, and migrating systematically.
Snowflake-to-Fabric migration assessment
Quantifying what you gain and what you give up. Not a product pitch in either direction.
Cutover planning & validation
Parallel-run windows, reconciliation testing, and rollback criteria so migration never means downtime for the business.
What it solves:
Organizations that migrate a fragmented Azure estate without an architecture plan create a more expensive mess than the one they started with. Migration is an architecture decision first and a lift-and-shift second.
What it includes:
Workspace topology design
Workspace structure, domain boundaries, and access patterns that map to your organization without creating governance debt.
Capacity planning & F-SKU sizing
Right-sizing based on actual CU consumption, not estimates from a Microsoft rep. If Copilot is on your roadmap, we size for its F64 minimum from the start.
Security architecture
Row-, column-, and object-level security across workspaces, semantic models, and OneLake domains.
Semantic model design
The semantic layer’s architecture, business relationships, and governance rules that every report inherits.
What it solves:
The wrong workspace topology creates governance debt that takes years to unwind. Capacity sizing has a floor — F64 if Copilot is on the roadmap, lower if it isn’t. We size to that floor, then optimize everything above it. That’s the difference between cost optimization and cost-cutting that quietly breaks AI features six months later.
Deliverable:
Architecture blueprint covering OneLake structure, workspace topology, security, capacity sizing, and semantic model design — the document your team references for the next two years, not a slide deck that gets filed away.
Fabric Architecture, Capacity Planning & Security
What it is:
The set of design decisions that determine whether your platform scales, performs, and stays governable as you grow — workspace topology, capacity sizing, and security configuration.
Data Transformation & Power BI Integration
What it is:
Turning centralized raw data into analysis-ready datasets — structured, validated, and modeled for the questions your business actually needs to answer.
What it includes:
Data Factory pipelines & dataflows
ELT workflows that keep data fresh, tested, and documented.
Fabric notebooks
PySpark and Python transformation workloads handled natively within Fabric.
dbt on Fabric
Version control, testing, and modular SQL for teams with existing [dbt workflows]
Dimensional modeling
Star schemas and data vault structures that serve both reporting and advanced analytics.
Semantic model implementation
Building the models defined in the architecture phase, developing DAX measures, and optimizing for Direct Lake performance.
Power BI integration
Migrating standalone Power BI Premium into Fabric and building report architectures that scale without duplicating datasets.
What it solves:
Unreliable data quality, broken pipelines, slow reports, and inconsistent metrics are all transformation failures. If your analysts spend more time waiting for refreshes than analyzing data, the cause is here — or in capacity sizing. Direct Lake performance depends on both being right. The architects who design the solution are the ones who build it.
What it includes:
AI-ready data modeling
Structuring OneLake data so Copilot can query it accurately without hallucinating metrics or misreading business definitions.
Semantic model optimization for Copilot
Power BI semantic models with clear business definitions so Copilot generates correct DAX instead of approximations.
Governance guardrails
Ensuring AI features never expose sensitive data to users who shouldn’t see it.
Copilot in Power BI enablement
Activating natural-language querying against your semantic models (requires F64 capacity minimum or Power BI Premium P1).
Fabric IQ business ontology
Defining business entities and relationships (customers, shipments, orders, locations) so Copilot and Azure OpenAI reason with business context instead of raw tables. *Note: Fabric IQ’s ontology feature is in preview — we implement it where it’s stable for your workload and say so where it isn’t.*
What it solves:
AI features on top of poorly modeled data produce confident wrong answers. A Copilot that contradicts your reports destroys analyst trust faster than no AI at all.
Copilot & AI Readiness on Microsoft Fabric
What it is:
Copilot is only as good as the data it can reach. Most Fabric environments are not structured for AI workloads. This engagement makes sure yours is — from day one.
Fabric Managed Services & Ongoing Support
Not every organization needs a full-time Fabric administrator. But every production Fabric environment needs ongoing attention.
- Continuous capacity monitoring and performance tuning.
- Quarterly architecture reviews as your data environment evolves.
- Pipeline maintenance and incident response.
- Copilot and feature enablement as Microsoft ships quarterly platform updates.
- On-call expertise when something breaks — not a job posting to write.
Managed services provide that depth at a fraction of the cost of a senior FTE. The engagement doesn’t end when the platform goes live. It ends when your team can run it confidently — or when you decide you’d rather have us keep running it for you.
Microsoft Fabric Capacity Optimization Consulting
The most common Fabric engagement we see: a company enabled Fabric, provisioned capacity, started building — and six months later is spending more than expected with nobody able to explain where the capacity units go.
Our capacity optimization audit analyzes actual CU consumption patterns, identifies workloads that can run off-peak, right-sizes your F-SKU against real usage (factoring in Copilot’s F64 minimum if it’s in use or on the roadmap), and implements monitoring, so your team has continuous visibility into where capacity is going.
The goal is simple: pay for what you use, keep what you need.
Early Fabric capacity audits have identified 25–45% in reclaimable CU spend — driven by right-sizing F-SKU tier, pausing non-production capacities outside business hours, and optimizing the heaviest data-loading workloads — without breaking a single scheduled job.
Microsoft Fabric Consulting for Mid-Market Companies
The Microsoft Fabric consulting market has a gap. Enterprise firms assign senior architects to the sales call, then staff projects with junior consultants still ramping up on the platform — with minimum engagements starting at $150K and 12–24 week procurement cycles. Offshore firms offer lower rates but deliver generic implementations with no senior oversight, timezone friction, and no incentive to tell you when Fabric isn’t the right fit. For a $10M–$100M company, neither model works.
Data-Sleek sits in a different position. We’re a boutique, senior-led firm with over 30 years of data architecture experience, working with Fabric since general availability. We’ve delivered engagements across healthcare, insurance, higher education, construction, and transportation — our recommendations are grounded in how these industries actually use data, not what the platform can do in theory.
The real differentiator is simpler: we implement both Snowflake and Fabric at a production level, and we’ll tell you honestly which one is right for your business. Most consultants can only recommend what they know. We recommend what fits.
Microsoft Fabric vs. Snowflake: When to Choose Each
Most Fabric consultants won’t write this section. We’ve built production systems on both platforms for years, and neither is universally better. Here’s how we actually advise clients.
Decision factor
Microsoft Fabric
Snowflake
Cloud availability
Azure only
Runs identically on AWS, Azure, and GCP
Pricing model
Capacity-based: fixed monthly cost for a defined F-SKU; predictable budgeting
Consumption-based: per-query compute scaling; costs can spike with query volume
BI layer
Native Power BI with Direct Lake mode, built-in semantic models, and Copilot
Connects to Power BI, Tableau, Looker, and others via standard connectors
Query-intensive workloads
Shared capacity pool; heavy transformation loads compete for CUs
Purpose-built query engine; per-warehouse scaling suits thousands of dbt models
AI capabilities
Copilot and Fabric IQ built into the platform — no separate infrastructure
Cortex AI functions; broader AI stacks typically assembled from components
Ecosystem fit
Strongest for Microsoft-first organizations: single sign-on, Purview governance, OneLake shared storage
Strongest for multi-cloud and best-of-breed stacks: Snowflake + dbt + Fivetran + your BI tool, each swappable
Best fit
Azure/Microsoft 365 shops standardized on Power BI that want one platform, one security model, predictable cost
Multi-cloud organizations with query-heavy, SQL-first workloads and established dbt pipelines
Choose Fabric when
you’re already deep in the Microsoft ecosystem and want a unified platform; you need predictable capacity-based pricing; your BI team lives in Power BI; or you want AI capabilities built into the platform rather than bolted on.
Choose Snowflake when
you need multi-cloud flexibility; your workloads are query-intensive with complex transformations; or you prefer modular, SQL-first workflows where any component can be swapped without re-platforming.
Choose both when
different business units have different platform needs, you’re migrating incrementally and want new workloads in Fabric while existing ones run on Snowflake, or you want Snowflake for heavy-duty warehousing with Fabric handling BI and AI.
Not sure whether Fabric or Snowflake is the right platform?
We’ll give you an honest answer.
Client Spotlight
In a $2.1 trillion U.S. construction insurance market, Tradesman Insurance needed a unified data warehouse to streamline reporting, reduce operational risk, and strengthen decision-making. Serving small businesses in a high-risk industry, the company relied on timely, accurate data to improve service delivery and customer retention. To overcome fragmented systems and slow reporting cycles, Tradesman partnered with Data-Sleek to modernize its analytics infrastructure and enable real-time visibility across the organization.
Explore the Tradesman insurance data warehouse case study →
Explore the Tradesman insurance data warehouse case study →
An innovative hearing aid company makes waves with a revolutionary product, but data management challenges prevented it from understanding customer needs. To bridge that gap, Data-Sleek partnered with Jabra Enhance to develop user-friendly dashboards that give both the company and its customers a 360-degree view of their health. Streamlined data led the company to secure a $94.8 million deal with one of the largest market players, supercharging growth.
See how we modernized Jabra Enhance's healthcare data platform →
See how we modernized Jabra Enhance's healthcare data platform →
Auto Rescue Solutions serves thousands of stranded drivers across the U.S., but fragmented dispatch systems and siloed data were stalling growth. Data-Sleek partnered with Auto Rescue Solutions to build a centralized data warehouse that unified call logs, GIS feeds, and contractor data, enabling predictive analytics that pre-positioned crews before calls came in and fueled expansion into thousands of new cities nationwide.
See how we built Auto Rescue Solutions' data warehouse →
See how we built Auto Rescue Solutions' data warehouse →
Why Choose Data-Sleek for Microsoft Fabric Consulting Services
We combine early Microsoft Fabric adoption with decades of data architecture expertise to deliver Microsoft Fabric environments that are fast, scalable, and cost-efficient from day one.
30+ Years of Data Architecture Experience
Our strength is engineering. With decades of database architecture and data modeling experience, we build Microsoft Fabric solutions that follow proven principles—ensuring speed, reliability, and predictable costs.
Industry Expertise That Matters
We serve five core industries—Healthcare, Insurance, Higher Education, Transportation, and Construction—designing Microsoft Fabric solutions tailored to their unique operational and regulatory needs.
U.S.-Based, Microsoft Fabric Consulting-Certified Team
Our consultants are senior-level, SnowPro-certified, and located across Los Angeles, Irvine, San Francisco, Dallas, Chicago, and New York, allowing us to support clients across all major U.S. time zones.
Independent, Architecture-Driven Guidance
We’re not influenced by vendor tiers. We provide honest, engineering-first advice and build Microsoft Fabric environments that scale cleanly without unnecessary complexity or cost.
Nationwide U.S. Coverage Across Multiple Time Zones
Our Microsoft Fabric-certified consultants are based across the United States, giving us broad geographic coverage and the ability to support clients in every major time zone. Whether you’re on the West Coast, East Coast, or anywhere in between, our team is positioned to collaborate in real time and deliver responsive, high-quality Microsoft Fabric consulting services.
Our distributed team—located in San Francisco, Los Angeles, Irvine, Dallas, Chicago, and New York—allows us to support national organizations seamlessly, adapt to regional needs, and stay aligned with your working hours, no matter where your teams or data operations are located.
Microsoft Fabric U.S.-Based Expertise That Scales With You
Because our consultants are fully U.S.-based, our clients benefit from:
- Real-time collaboration during U.S. business hours
- Faster response times
- Fewer communication hurdles
- Stronger alignment with regulatory and compliance requirements
- Deep familiarity with U.S. industry standards across healthcare, insurance, higher education, transportation, and construction
With full U.S. coverage and deep industry expertise, we deliver Microsoft Fabric solutions with the clarity, speed, and precision your team requires.
Our Microsoft Fabric Technology Stack
We build with whatever combination of technologies delivers the best architecture for your business — not whatever’s in the Microsoft catalog.
Core platform — Microsoft Fabric:
OneLake (unified storage), Data Factory (pipelines and dataflows), Fabric Data Engineering (notebooks and Spark), Fabric Data Warehouse (SQL analytics), and Power BI (reporting and semantic models). One platform, one security model, one storage layer.
Data transformation — dbt + Fabric notebooks:
dbt brings version control, testing, and modular SQL to Fabric transformations. Fabric notebooks handle PySpark and Python workloads natively.
Data ingestion — Fabric Data Factory + Fivetran:
Data Factory handles Microsoft and Azure sources natively; Fivetran provides pre-built connectors for the hundreds of non-Microsoft sources your organization relies on — Salesforce, HubSpot, and more — eliminating custom pipeline development.
Visualization — Power BI + Tableau:
Power BI is the native choice in Fabric — Direct Lake mode, Copilot integration, and native semantic models make it the most tightly integrated BI option available. Tableau connects via DirectQuery for organizations that have standardized on it.
Cloud layer — Azure + multi-cloud bridge:
Fabric runs on Azure. For organizations that also operate on AWS or GCP, Snowflake provides the multi-cloud bridge for cross-cloud data sharing.
AI/ML — Copilot, Fabric IQ, Azure AI:
Fabric IQ provides the business semantic layer that powers Copilot and Azure OpenAI. Built into the platform, not bolted on.
Frequently Asked Questions
Have a question?
What does a Microsoft Fabric consultant do?
A Microsoft Fabric consultant designs, implements, and optimizes data platforms built on Fabric: OneLake architecture, workspace topology, Data Factory pipeline development, semantic model creation, security configuration, capacity planning, and Power BI integration. The difference between a Fabric consultant and a license reseller is focus — a reseller offers implementation as part of a broader Microsoft relationship; a consultant architects production systems and tells you when Fabric isn’t the right platform for your workload.
How much does Microsoft Fabric consulting cost?
Cost depends on the engagement type. Discovery and assessment engagements are shorter with a lower investment. A full implementation — typically 8–12 weeks for mid-market companies — scales with scope: number of data sources, migration complexity, security requirements, and BI layer design. Ongoing managed services run as a monthly retainer at a fraction of the cost of a senior full-time hire. Unlike enterprise consultancies with $150K–$500K minimums and months-long procurement cycles, we scope to what you actually need and start work in days, not quarters.
How long does a Microsoft Fabric implementation take?
Typical mid-market implementations run 8–12 weeks from kickoff to production. Enterprise-scale migrations run 16–20 weeks. Strategy and assessment engagements complete in 2–4 weeks and produce a platform recommendation, architecture direction, and cost projection before any build begins.
How do I choose a Microsoft Fabric consulting partner?
Ask five questions. Are they vendor-neutral, or do they earn referral revenue for recommending Fabric? Who works on the engagement — senior architects or junior consultants? Can they show case studies with named clients and measurable outcomes? Can they implement alternatives like Snowflake and dbt, or is Fabric the only platform they know? And where is their team — U.S.-based with timezone alignment, or offshore?
Should I migrate from Snowflake to Microsoft Fabric?
It depends on your ecosystem, workloads, and team. If you’re Azure-first, your BI team has standardized on Power BI, and you want a unified platform under one security model, Fabric is worth serious evaluation. But if you need multi-cloud flexibility or your transformation layer is built on Snowflake SQL with hundreds of dbt models, migration introduces risk that may not justify the consolidation benefits. We’ve built on Snowflake for 8+ years and can quantify exactly what you’d gain and what you’d give up. Learn more about our Snowflake consulting services.
Can you optimize my existing Microsoft Fabric environment?
Yes — it’s one of the most common engagements we run. We audit actual CU consumption patterns and right-size your F-SKU based on real usage, factoring in Copilot’s F64 minimum if it’s in use or on the roadmap. The goal: pay for what you use, keep what you need.
About Data-Sleek
Data-Sleek is a U.S.-based data & AI consulting firm that helps organizations turn raw information into measurable business value. We specialize in data architecture, strategy, integration, and warehousing—empowering businesses to scale with confidence, maintain compliance, and achieve operational excellence.
Ready to build a production-grade data platform on Microsoft Fabric?
Talk to a senior data architect — not a sales rep.
