Microsoft Fabric Consulting Services.pdf

Discover how Dobbs Data’s expert Microsoft Fabric Consulting Services help businesses accelerate their journey from raw data to actionable insights. This guide explores modern data integration, analytics, and visualization strategies that empower smarter, faster decision-making. Perfect for organizations seeking to unlock the full potential of their data with Microsoft Fabric.

2. Microsoft Fabric Consulting Services:
Accelerate Your Data to Decisions

If you’re exploring Microsoft Fabric consulting services, you’re likely comparing providers and
trying to pinpoint what’s included, how fast you’ll see value, and what pitfalls to avoid. This guide
goes deeper than typical service pages with a practical roadmap, migration tips, governance
essentials, and ROI levers—so you know exactly what to expect.
What is Microsoft Fabric—and why it
matters now
Microsoft Fabric is a unified analytics platform that brings together data integration, engineering,
science, real-time analytics, and BI in one SaaS experience.
Key building blocks include:
 OneLake (a single, governed data lake for your org)
 Lakehouse and Warehouse (Delta-backed analytics storage)
 Data Engineering and Data Factory (Spark and pipelines)
 Real-Time Analytics (KQL databases and event processing)
 Power BI and Semantic Models (including Direct Lake mode)
 Data Activator (no-code triggers for actions)
 Copilot in Fabric (AI-assisted development and insights)
Why it matters:
 One platform reduces integration overhead and tooling sprawl.
 Direct Lake eliminates many dataset refreshes and speeds up BI.
 Governance, security, and lineage are built-in with Microsoft Purview.
 SaaS model means faster deployments and lower ops burden.

3. What you get from Microsoft Fabric
consulting services
A good partner accelerates time-to-value and reduces risk. Expect help with:
 Strategy and architecture: Tenant setup, workspace strategy, domain design,
medallion/lakehouse patterns.
 Cost management: Capacity planning (F SKUs), autoscale policies, Direct Lake
optimization.
 Migration: From Azure Synapse, ADF, and Power BI Premium to Fabric workloads.
 Implementation: Pipelines, notebooks, lakehouse/warehouse, semantic models, and
dashboards.
 Governance and security: RBAC, sensitivity labels, RLS/OLS, data product standards,
DevOps.
 Enablement: Playbooks, training, and change management to boost adoption.
Pro tip: Ask for a “first-90-days plan” and success metrics upfront.
A proven roadmap: from assessment to
scale
Phase 1 — Readiness and Value Design (1–2 weeks)
 Current state and goals: Inventory sources, BI use cases, and pain points.
 Tenant and capacity: Choose Fabric F SKU(s), cost model, autoscale, and pause
policies.
 Architecture and governance: Domains, workspaces, OneLake layout, medallion layers,
RBAC model.
 Security and compliance: Sensitivity labels, RLS/OLS, data loss prevention, Purview
scanning.
 Prioritized use cases: Pick a 2–4 week pilot with clear ROI and adoption metrics.

4. Deliverables:
 Target architecture diagram
 90-day adoption plan and success KPIs
 Capacity and cost estimation
Phase 2 — Pilot and Accelerator Build (2–4 weeks)
 Data onboarding: Land raw data into OneLake. Use pipelines or Dataflows Gen2.
 Engineering: Build a lakehouse with Delta tables, notebooks, and medallion
transformations.
 BI model: Create a semantic model with Direct Lake; implement RLS and calculation
groups.
 Dashboarding: Power BI reports for exec and analyst personas.
 Automation and DevOps: Git integration, deployment pipelines, and monitoring.
 Optional: Real-time analytics with KQL DB if streaming is in scope. Data Activator for
alerts.
Deliverables:
 Working pilot with documented patterns
 CI/CD and workspace standards
 Playbooks for repeatable data products
Phase 3 — Scale, Govern, and Operate (4–8 weeks)
 Industrialization: Templatize lakehouse/warehouse, patterns, and naming standards.
 Data product catalog: Publish to Purview with data contracts and ownership.
 Performance and cost: Optimize partitioning, file sizes, and semantic model design.
 Self-service: Curated data hub, certified datasets, and semantic links for reuse.
 Training: Role-based enablement for engineers, analysts, and data owners.
Deliverables:
 Operable platform with SLAs/alerts
 Governance board rituals and backlog
 Adoption dashboard and monthly FinOps review

5. Migration playbook: from Synapse and
Power BI to Fabric
Most organizations aren’t starting from zero. Here’s a pragmatic path:
 Inventory and classify: Pipelines, notebooks, SQL pools, datasets, and reports.
 Map workloads:
o Synapse pipelines → Fabric Data Factory
o Spark notebooks → Fabric Data Engineering
o Dedicated SQL → Fabric Warehouse or Lakehouse SQL endpoint
o Power BI datasets → Fabric semantic models (Direct Lake where feasible)
 Direct Lake first: Reduce refresh complexity and costs. Validate model size and
performance.
 Replatform smartly: Migrate high-value use cases first. Keep some legacy until de-
risked.
 Validate governance: Re-implement RLS/OLS, sensitivity labels, endorsements, and
permissions.
 Test end-to-end: Data, security, performance, and business outcomes—not just
technical parity.
Common pitfalls to avoid:
 Under-sizing capacity and skipping autoscale.
 Lifting-and-shifting flawed models vs. redesigning for Direct Lake.
 Ignoring workspace strategy and mixed dev/prod environments.
Governance, security, and cost control in
Fabric
Nail this early to avoid rework:
 Workspace and domain design: Align to data products and ownership. Separate
dev/test/prod.

6.  OneLake standards: Clear folder structure, naming, and medallion conventions.
 Security: RBAC, managed identities, private endpoints (as applicable), RLS/OLS in
models.
 Compliance: Sensitivity labels, data retention, data residency, Purview lineage and
scanning.
 FinOps: Capacity sizing, autoscale, pause policies, Direct Lake adoption, refresh
scheduling, cost dashboards.
Industry use cases we see winning fast
 Retail: Unified demand forecasting, inventory optimization, store performance insights.
 Manufacturing: IIoT telemetry with Real-Time Analytics, yield analytics, maintenance.
 Healthcare: Quality measures, payer analytics, protected data governance.
 Financial services: Risk and fraud monitoring, liquidity dashboards, compliant reporting
Who’s on the project team
 Fabric Solution Architect
 Data Engineer / Analytics Engineer
 Power BI Developer
 Platform/Governance Lead
 Optional: Data Scientist, MLOps, Real-Time/KQL specialist
 Engagement Manager for outcomes and adoption
FAQs
Q: How is Microsoft Fabric licensed?
A: Fabric uses capacity-based F SKUs billed by v-core hour. You can autoscale or pause to
control spend. Some features require specific SKUs—validate in Microsoft docs.
Q: Do we need to abandon Databricks or Synapse entirely?
A: Not necessarily. Many run a coexistence model while consolidating net-new workloads in
Fabric. Your partner should help define a pragmatic, staged approach.

7. Q: What is Direct Lake, and why is it a big deal?
A: Direct Lake lets Power BI semantic models read Delta tables in OneLake without scheduled
refresh, reducing latency, complexity, and cost for large datasets.
Q: How quickly can we see value?
A: Most teams ship a credible pilot in 2–4 weeks with the right scope and data access. Full
scale and governance maturity typically follow in 8–12 weeks.