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Microsoft Fabric - A Practical Guide to the Unified Data Platform

February 20, 2026•7 min read•Michael Ridland

Every few years, Microsoft reshuffles the data platform deck. Power BI here, Synapse there, Data Factory over there, Purview somewhere else. Different portals, different billing, different admin experiences.

Fabric is Microsoft's answer to that fragmentation. One platform, one capacity model, one lake. Whether it's the right move for your organisation depends on where you're starting from and what you're trying to achieve.

What Fabric Actually Is

Microsoft Fabric is a unified analytics platform that brings together data engineering, data science, real-time analytics, data warehousing, and business intelligence under a single SaaS experience.

The key difference from previous Microsoft data offerings isn't any single capability. It's the integration. Everything shares:

  • OneLake: A single data lake for the entire organisation. Think of it as the OneDrive for your analytical data. One copy of data, accessible by all workloads.
  • Unified capacity model: One pool of compute that all workloads draw from. No more provisioning separate clusters for Synapse, separate capacity for Power BI, separate integration runtimes for Data Factory.
  • Shared security and governance: One set of policies, one admin portal, one lineage view.
  • Common experience: Same workspace, same collaboration model, regardless of whether you're building a data pipeline, a warehouse query, or a Power BI report.

OneLake: The Foundation

OneLake is arguably the most important piece. It's built on Delta Lake (Parquet + transaction log), which means your data is stored in an open format that multiple engines can read.

Why this matters:

  • No more data copies: In the old world, you'd copy data from blob storage into Synapse, then into Power BI datasets. Each copy meant cost, latency, and governance headaches. OneLake stores data once.
  • Shortcuts: You can create references to data in external sources (Azure Data Lake Gen2, AWS S3, Google Cloud Storage) without moving it. Your existing data lake doesn't have to be abandoned.
  • Direct Lake mode: Power BI can query Delta tables in OneLake directly, without importing data into a dataset. This eliminates the dataset refresh bottleneck that plagues large Power BI deployments.

If you're running into Power BI refresh limits or managing multiple copies of the same data, OneLake alone might justify evaluating Fabric.

The Workloads

Fabric bundles several workloads, each serving a different persona:

Data Engineering

Spark-based notebooks and lakehouses for data engineers. If you've used Synapse Spark pools or Databricks notebooks, the experience is familiar. You write PySpark or SQL to transform data in the lakehouse.

When it fits: Teams already using Spark who want tighter integration with Power BI and Data Factory.

Data Factory

Pipeline orchestration and data movement. Fabric's Data Factory is essentially ADF (Azure Data Factory) running natively in Fabric, with access to OneLake and Dataflows Gen2.

When it fits: Teams with existing ADF pipelines who want to consolidate into Fabric. Our Data Factory consultants can help you plan the migration path.

Data Warehouse

A T-SQL-based warehouse experience built on the same Delta Lake storage. Write SQL, create tables and views, run queries. No cluster management.

When it fits: Teams with SQL Server or Synapse SQL expertise who want warehouse capabilities without infrastructure management.

Real-Time Analytics

Event-driven analytics using KQL (Kusto Query Language). Built for streaming scenarios: IoT telemetry, application logs, clickstreams.

When it fits: Organisations with real-time data needs that currently use Azure Data Explorer or Synapse Data Explorer.

Power BI

The same Power BI you know, but deeply integrated with the rest of Fabric. Direct Lake mode, shared workspaces, and unified governance make it more powerful inside Fabric than standalone.

Our Power BI consultants help organisations get the most from Power BI, whether standalone or as part of a Fabric deployment.

Data Science

Notebooks with MLflow integration for machine learning. Train models, track experiments, deploy to production.

When it fits: Data science teams who want integrated ML alongside their data engineering and BI workflows.

Fabric vs Synapse vs Databricks

The question everyone asks: should I use Fabric, stick with Synapse, or go with Databricks?

Choose Fabric When

  • You're a Microsoft shop (M365, Azure, Power BI) and want the tightest integration
  • You want a single platform with unified billing and governance
  • Your team spans BI analysts and data engineers and you want them in one workspace
  • You're hitting Power BI dataset refresh limits and need Direct Lake
  • You prefer a fully managed SaaS experience over infrastructure management

Consider Staying with Synapse When

  • Your current Synapse implementation is stable and meeting needs
  • You have heavy Synapse SQL Dedicated Pool workloads with specific performance requirements
  • Migration risk outweighs consolidation benefit right now

Consider Databricks When

  • You need best-in-class Spark performance for large-scale data engineering
  • Your team has deep Databricks expertise
  • You want maximum portability across clouds (AWS, Azure, GCP)
  • Advanced ML/AI workloads are your primary concern

The honest answer: Fabric is the right default for organisations already invested in Microsoft. Databricks is the right choice when Spark performance and multi-cloud portability matter more than Microsoft integration. Synapse as a standalone product is increasingly being absorbed into Fabric.

Migration Considerations

If you're considering Fabric, here's what to think about:

From Power BI Premium

This is the most natural path. Power BI Premium capacity converts to Fabric capacity. Your existing reports, datasets, and workspaces carry over. You gain access to Fabric workloads on the same capacity.

Watch for: Capacity sizing. Fabric workloads share compute with Power BI. If your Premium capacity is already stretched, adding Fabric workloads may require upsizing.

From Azure Synapse

Synapse pipelines and Spark notebooks can be migrated to Fabric, but it's not automatic. SQL dedicated pools don't have a direct equivalent in Fabric's warehouse.

Watch for: Feature gaps. Fabric is evolving rapidly but may not yet match every Synapse capability. Evaluate your specific workloads.

From Azure Data Factory

ADF pipelines can be migrated to Fabric Data Factory. The concepts are the same, but some connectors and features may differ.

Watch for: Connector availability. Fabric Data Factory supports most ADF connectors, but check your specific sources and sinks.

From On-Premises SQL Server / SSIS

This is a bigger lift. SSIS packages need to be re-architected as Fabric pipelines or Dataflows Gen2. SQL Server databases need to be migrated to Fabric warehouse or lakehouse.

Watch for: This is a modernisation project, not a migration. Plan accordingly.

Common Mistakes

Mistake 1: Treating Fabric as "just Power BI Premium". Fabric is a data platform. If you use it only for Power BI, you're paying for capabilities you're not using. Have a plan to adopt additional workloads.

Mistake 2: Migrating everything at once. Start with one workload. Get it stable. Expand. Trying to move data engineering, warehousing, and BI simultaneously creates too much risk.

Mistake 3: Ignoring governance from the start. OneLake makes data accessible across workloads. That's powerful, but it also means security policies need to be right from day one. Don't retrofit governance.

Mistake 4: Expecting zero learning curve. Fabric's individual components are familiar, but the integrated model is new. Budget time for your team to learn how things connect.

Mistake 5: Over-provisioning capacity. Fabric's capacity model is flexible, but it's easy to over-buy. Start conservative, monitor usage, and scale up based on actual demand.

Getting Started

If you're evaluating Fabric for your organisation:

Step 1: Assess your current state. What data tools are you running today? What's working? What's painful? This determines your migration priority.

Step 2: Start with a Fabric trial. Microsoft offers trial capacity. Use it to explore, not to build production workloads. Get your team familiar with the experience.

Step 3: Pick one workload. For most organisations, this is Power BI with Direct Lake. It delivers immediate value (faster refreshes, larger datasets) with relatively low risk.

Step 4: Build your OneLake strategy. Decide what data lives in OneLake natively and what stays external via shortcuts. This is an architectural decision that affects everything downstream.

Step 5: Expand deliberately. Add Data Factory pipelines, then data engineering workloads, then warehousing. Each step should deliver measurable value before you move to the next.

As Microsoft Fabric consultants, we help Australian businesses evaluate, plan, and implement Fabric in a way that delivers value without unnecessary risk. Whether you're migrating from Synapse, consolidating from multiple tools, or starting fresh, we can help you build a data platform that actually works.

Let's talk about your data platform strategy.