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Microsoft Fabric & Analytics

One platform for data engineering, warehousing, and visualization — eliminating integration purgatory.

The End of Fragmented Analytics

Historically, building an enterprise analytics suite meant managing billing and integration complexities across a maze of disconnected tools — Azure Data Factory for ETL pipelines, a separate Data Lake for storage, Synapse for SQL processing, and Power BI for visualization. Pushing data across those service boundaries created immense technical debt, networking headaches, and authentication complexity that consumed more engineering time than actual data analysis.

Microsoft Fabric changes everything. As a unified SaaS analytics platform, it consolidates data engineering, data warehousing, real-time analytics, data science, and business intelligence into a single experience built on a foundational shared storage layer called OneLake. Your data engineers, data scientists, and business analysts all work on the exact same data — no copying, no format conversion, no integration pipelines between tools.

For organizations already invested in the Microsoft ecosystem, Fabric represents the natural evolution of their data strategy. It inherits the security model from Entra ID, the governance from Purview, and the visualization from Power BI — while providing dramatically simplified administration through consumption-based capacity units rather than managing individual Azure PaaS service configurations.

Microsoft Fabric & Analytics

Why Organizations Move to Fabric

Ignoring these challenges compounds technical debt at an exponential rate — every quarter of inaction multiplies the eventual remediation cost.

01

Data Duplication Costs

Traditional analytics architectures require copying datasets five or more times — from source to landing zone, to staging, to warehouse, to Power BI import model. Fabric's DirectLake mode eliminates this duplication entirely, querying Parquet files directly from OneLake without data movement.

02

Integration Headaches

Maintaining network peering, managed identities, and authentication bridges between Azure Data Factory, Synapse, ADLS Gen2, and Power BI consumes weeks of engineering effort for every new data pipeline. Fabric collapses these into a single authenticated workspace.

03

PaaS Complexity

Managing individual Azure PaaS service scaling, patching, and billing across Synapse Serverless, Dedicated SQL Pools, Spark Clusters, and ADF Integration Runtimes requires deep specialized knowledge. Fabric abstracts this into simple SaaS capacity purchasing.

What We Deliver

Modern Microsoft ecosystem capabilities engineered for enterprise-scale transformation and measurable productivity improvement.

01

OneLake Architecture Design

Deploying Microsoft's foundational 'OneDrive for Data' architecture — a single, multi-format data lake that serves as the unified storage layer for your entire analytics stack. We design the workspace hierarchy, lakehouse structure, and access control model that organizes your data logically while maintaining strict security boundaries between business units.

Delta-Parquet table design providing ACID transactions on top of lakehouse storage
Shortcut integrations connecting OneLake to external data sources including AWS S3, Azure Storage, and Google Cloud Storage
Workspace domain groupings aligned to organizational boundaries with granular access controls
OneLake data governance using Purview integration for sensitivity labeling, classification, and lineage tracking
02

Data Engineering & Pipelines

Building the ingestion and transformation logic that feeds your lakehouse from dozens of source systems. We design Fabric Data Factory pipelines and Spark notebooks that extract, cleanse, and model raw operational data into analytically optimized structures — implementing the medallion architecture (Bronze → Silver → Gold) within Fabric's unified environment.

Fabric Data Factory pipelines adapted from existing ADF with copy activities, mapping data flows, and scheduled triggers
PySpark notebook development for complex transformation logic requiring distributed computing
Incremental loading patterns processing only changed records to minimize compute consumption and refresh time
Data quality validation using Great Expectations or custom Spark assertions at every medallion layer boundary
03

Real-Time Intelligence

Ingesting and analyzing massive streaming data — IoT sensor arrays, application telemetry, financial transaction feeds — with sub-second latency. Fabric's Real-Time Analytics uses KQL (Kusto Query Language) databases and Eventstreams to provide live operational dashboards updated in real-time rather than batch-refreshed hourly.

Eventstream configuration capturing live data from Event Hubs, IoT Hub, Kafka, and custom sources
KQL database deployment for high-velocity time-series ingestion and analytical querying
Real-time dashboard creation with auto-refreshing visuals connected to KQL queries
Reflex alert configuration triggering automated actions when real-time metrics cross defined thresholds
04

Power BI DirectLake & Semantic Models

Obliterating traditional Power BI dataset import limitations by enabling dashboards to read OneLake data directly in memory — combining the performance of import mode with the freshness of DirectQuery. We design semantic models that serve as the curated business logic layer between raw data and executive consumption.

DirectLake mode configuration eliminating scheduled refresh dependencies for always-current dashboards
Semantic model design with calculated measures, hierarchies, and business logic centralized for reuse across reports
Row-Level Security implementation ensuring executives see only their region or business unit data
Deployment pipeline configuration for promoting dashboard changes through dev → test → production stages

Fabric Modernization Framework

A practical path from fragmented Azure analytics to unified Fabric intelligence.

01

Assessment & Strategy

We evaluate your existing data infrastructure — Synapse, ADF, ADLS, Power BI workloads — and design the migration path to Fabric. We identify which workloads benefit most from unification, which can migrate as-is, and which require refactoring to leverage Fabric-native capabilities.

02

Lakehouse Design

We architect the OneLake structure — workspace hierarchy, lakehouse tables, security boundaries, and shortcut connections to external sources. We implement the medallion architecture and configure data governance policies ensuring Purview classification flows through to every layer.

03

Pipeline Migration

We adapt existing ADF pipelines and Synapse notebooks into Fabric-native Data Factory and Spark workflows. We implement incremental loading, error handling, and monitoring — validating data quality at every transformation stage against production baselines.

04

BI Modernization

We convert Power BI import models to DirectLake semantic models, eliminating refresh schedules and data staleness. We redesign dashboards to leverage Fabric's enhanced performance and deploy governance controls for workspace management and report distribution.

What You Receive

Every engagement produces concrete, actionable deliverables — not theoretical frameworks that require additional investment to become useful.

Fabric Migration Assessment

A detailed technical assessment comparing your current Azure analytics architecture against Fabric capabilities — including cost modeling, feature gap analysis, and a prioritized migration roadmap with effort estimates per workload.

Lakehouse Architecture Document

A comprehensive design document defining workspace structure, table schemas, partitioning strategies, security boundaries, shortcut configurations, and Purview integration — serving as the blueprint for your unified data platform.

Pipeline Documentation & Runbooks

Operational documentation for every data pipeline including source-to-target mappings, transformation logic, error handling procedures, monitoring dashboards, and troubleshooting guides for the operations team.

Executive BI Suite

A production-ready collection of Power BI dashboards using DirectLake mode — covering the KPIs, dimensions, and drill-through paths defined during requirements gathering, with RLS security and automated subscription delivery.

Frequently Asked Questions

Do we have to rewrite all our Azure Data Factory pipelines to use Fabric?
No. Fabric incorporates Data Factory natively, and most standard ADF pipelines — including copy activities, mapping data flows, and scheduled triggers — migrate into Fabric with minimal modifications. The key changes involve updating linked services and connection references to point to OneLake rather than ADLS Gen2.
What is the difference between Microsoft Fabric and Azure Synapse?
Azure Synapse is a PaaS analytics service requiring you to manage individual compute resources, networking, and service integration. Microsoft Fabric is a SaaS platform that bundles Synapse capabilities (SQL, Spark), Data Factory, Power BI, and real-time analytics into a single unified experience with simplified capacity-based billing.
Is Fabric suitable for real-time analytics or only batch processing?
Both. Fabric includes a dedicated Real-Time Analytics experience with Eventstreams and KQL databases purpose-built for streaming data at massive scale. This is powered by the same Kusto engine that underlies Azure Data Explorer, handling millions of events per second with sub-second query latency.
How does Fabric pricing work compared to managing individual Azure services?
Fabric uses capacity units (CU) purchased at the tenant level. All workloads — pipelines, SQL queries, Spark jobs, Power BI refreshes — consume from this shared capacity pool. This simplifies billing dramatically compared to managing separate Synapse DWU, Spark cluster, and ADF integration runtime costs — though proper capacity planning is essential to avoid throttling.

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