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One-Pager Playbook

Microsoft Fabric — The V4 Data And AI Solutions Playbook

A share-with-your-team summary of how V4 Data And AI Solutions architects and optimizes Microsoft Fabric platforms for enterprise scale.

V4
V4 Data And AI Solutions
bhangare.vishwajeet@v4dataai.com

Why Microsoft Fabric

Microsoft Fabric consolidates Data Factory, Synapse Data Warehouse, Data Engineering, Real-Time Intelligence, Data Science and Power BI into a single SaaS on top of OneLake — collapsing six vendors into one governed platform with Purview + workspace RBAC + row/column-level security.

Pillar
OneLake Lakehouse

Bronze / Silver / Gold Medallion with Delta and adaptive Liquid Clustering.

Pillar
Fabric Warehouse

T-SQL warehouse with dbt-fabric transformations and Direct Lake to Power BI.

Pillar
Real-Time Intelligence

Event Streams + Eventhouse (KQL) + Reflex for sub-5s operational analytics.

Deep-Dives

82% Faster Query Execution on Microsoft Fabric OneLake

Technology · 6 months
82%
faster query execution
Reference Architecture
Sources: SQL Server (on-prem)→Ingest: Azure Data Factory→Lakehouse: Microsoft Fabric OneLake→Transform: PySpark (skew-tuned)→Serve: Fabric Warehouse
Before
  • Query latency30–60 min
  • Data scanned/query~1.2 TB
  • Compute per refreshF64 saturated
  • Sources unified0
After
  • Query latency<30 sec
  • Data scanned/query~35 GB
  • Compute per refresh~17% of F64
  • Sources unified20+

Microsoft Fabric Warehouse: Adaptive Liquid Clustering Cuts Compute 83%

Technology · 5 months
83%
lower compute consumption
Reference Architecture
Sources: ADF pipelines→Ingest: Dataflows Gen2→Lakehouse: Fabric OneLake→Warehouse: Fabric Warehouse (T-SQL)→Serve: Power BI Direct Lake
Before
  • Compute capacityF64 · saturated
  • Data scanned/queryTB-scale
  • Analyst waitMulti-hour
  • ClusteringNone
After
  • Compute capacityF32 · headroom
  • Data scanned/query-97%
  • Analyst waitSub-minute
  • ClusteringAdaptive Liquid

OneLake Multi-Cloud Consolidation: 20+ Sources into One Governed Platform

Technology · 7 months
20+
sources unified
Reference Architecture
Sources: AWS S3→Virtualize: OneLake Shortcuts (S3/ADLS)→Ingest: ADF + Dataflows Gen2→Lakehouse: Fabric OneLake · Delta→Govern + Serve: Purview + Unity Catalog
Before
  • ETL vendors6
  • New-source onboardingWeeks
  • Sources unified0
  • Pipeline reliability~70%
After
  • ETL vendors0 (native)
  • New-source onboardingHours
  • Sources unified20+
  • Pipeline reliability100%

Real-Time Intelligence on Fabric: Sub-Second Retail Ops Analytics

Retail · 4 months
<5s
event → alert latency
Reference Architecture
Events: POS terminals→Ingest: Fabric Event Streams→Real-time: Eventhouse (KQL DB)→Lakehouse: OneLake Shortcut → Delta→Serve: Power BI Real-time
Before
  • Event → alert latencyMinutes
  • Vendors6 (Kafka + custom)
  • Engineer change timeDays
  • Historical + real-timeSeparate
After
  • Event → alert latency<5 sec
  • Vendors1 (Fabric)
  • Engineer change timeHours
  • Historical + real-timeUnified
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