Quick Answer: What Is Microsoft Fabric?
Microsoft Fabric is a unified SaaS analytics platform that combines data engineering, data warehousing, real-time analytics, data science, and Power BI into a single platform with shared OneLake storage. It replaces the need for separate Azure services like Synapse Analytics, Data Factory, Data Lake Storage, and Power BI Premium by consolidating them into one integrated experience with one licensing model.
Fabric was announced at Microsoft Build in May 2023 and became generally available in November 2023. It represents the most significant change to Microsoft's data platform strategy in a decade. Every workload in Fabric — from Spark-based data engineering to T-SQL-based warehousing to Power BI reporting — reads from and writes to a single copy of data stored in OneLake in open Delta Parquet format. This eliminates the data silos, duplicated storage, and complex ETL pipelines that plague traditional analytics architectures.
For enterprises, the value proposition is straightforward: instead of managing 5-10 separate Azure services with different billing models, security configurations, and administration interfaces, you manage one platform. I have been working with Fabric since the private preview, and the organizations I have helped migrate from Azure Synapse to Fabric have seen a 30-50% reduction in total analytics infrastructure cost and a significant decrease in administration overhead.
Fabric pricing starts at $262.80/month for the F2 capacity SKU on pay-as-you-go. Organizations with existing Power BI Premium P1 or higher already have Fabric access included. For a detailed pricing breakdown, see the pricing section below.
What You Will Find in This Guide
2. Before Fabric vs. With Fabric: Architecture Comparison
The fastest way to understand Fabric's value is to compare what a typical enterprise analytics architecture looked like before Fabric with what it looks like after migrating. I have led dozens of these migrations for organizations ranging from 500 to 50,000 employees, and the reduction in architectural complexity is dramatic.
| Capability | Before Fabric (Traditional Azure) | With Microsoft Fabric |
|---|---|---|
| Data Ingestion | Azure Data Factory (separate service, separate billing) | Data Factory (built into Fabric, shared capacity) |
| Data Lake Storage | Azure Data Lake Storage Gen2 (provision, configure ACLs, manage keys) | OneLake (automatic, one per tenant, no configuration) |
| Data Engineering | Azure Synapse Spark pools or Azure HDInsight (manage clusters) | Fabric Spark (serverless, auto-scaling, no cluster management) |
| Data Warehouse | Azure Synapse dedicated SQL pool ($1.20/DWU/hour minimum) | Fabric Warehouse (serverless T-SQL, no pool management) |
| Real-Time Analytics | Azure Data Explorer or Azure Stream Analytics (separate services) | Real-Time Intelligence (KQL, Eventstreams, built-in) |
| Data Science / ML | Azure Machine Learning (separate workspace, compute, endpoints) | Fabric Data Science (notebooks, MLflow, shared OneLake data) |
| Business Intelligence | Power BI Premium (separate capacity, separate admin portal) | Power BI (integrated, DirectLake mode, shared capacity) |
| Security Model | Configure separately for each service (RBAC, ACLs, firewall rules) | Unified security at workspace and item level |
| Billing | 5-10 separate Azure meters, unpredictable monthly costs | One capacity SKU, predictable monthly cost |
| Admin Experience | Azure Portal + Power BI Admin Portal + multiple service consoles | Single Fabric Admin Portal |
The difference is not incremental — it is a fundamental shift in how you architect analytics. In my experience working with enterprise deployments, the organizations that benefit most from Fabric are those currently running 3 or more separate Azure analytics services. If you are managing Azure Synapse, Data Factory, Data Lake Storage, and Power BI Premium as separate services today, Fabric consolidates all of that into one product.
The cost savings come from two places: reduced infrastructure spend (one capacity instead of multiple Azure meters) and reduced administration time (one platform to manage instead of five). For a 1,000-person organization, I typically see a 30-40% reduction in total cost of ownership within the first year of Fabric adoption.
3. The 7 Microsoft Fabric Workloads Explained
Fabric organizes its capabilities into seven distinct workloads. Each workload is designed for a specific persona — data engineers, data analysts, data scientists, or business users — but they all share the same OneLake storage and Fabric capacity. Here is what each one does and when you would use it.
1. Data Engineering
What it does: Provides Apache Spark-based notebooks and lakehouse environments for transforming raw data into curated, analytics-ready datasets. You write PySpark, Spark SQL, Scala, or R code in Fabric notebooks that execute on fully managed Spark clusters.
Key concept — Lakehouse: The Fabric Lakehouse is a data architecture that combines the flexibility of a data lake with the structure of a data warehouse. Data lands in OneLake in Delta Parquet format, and you can query it using both Spark (for data engineering) and T-SQL (via the automatic SQL analytics endpoint). This dual-engine access eliminates the traditional ETL step of copying data from lake to warehouse.
Who uses it: Data engineers building data pipelines, transforming raw files into curated tables, and managing lakehouse medallion architectures (bronze/silver/gold layers).
2. Data Warehouse
What it does: A fully managed, serverless T-SQL data warehouse that stores data in OneLake. Unlike Azure Synapse dedicated SQL pools, there are no clusters to provision or pause. You write standard T-SQL to create tables, views, stored procedures, and functions. The warehouse auto-scales compute based on query demand.
Key advantage: Because both the Lakehouse and the Warehouse store data in OneLake as Delta Parquet, you can query lakehouse tables from the warehouse and vice versa using cross-database queries. This removes the historical barrier between lake and warehouse teams.
Who uses it: SQL developers, BI analysts, and data architects who prefer T-SQL over Spark and need traditional warehouse capabilities like stored procedures and views.
3. Data Factory
What it does: The data integration and orchestration engine. Fabric Data Factory includes data pipelines (similar to Azure Data Factory pipelines) and dataflows (Power Query-based transformations). It connects to 170+ data sources and moves data into OneLake.
Key advantage: If your team already uses Azure Data Factory, the pipeline experience in Fabric is nearly identical — same activities, same expression language, same monitoring. Migration from ADF to Fabric Data Factory is straightforward, and pipelines can be moved with minimal changes.
Who uses it: Data engineers and ETL developers who need to ingest data from external systems (databases, SaaS applications, files) into OneLake on a schedule.
4. Data Science
What it does: Provides Jupyter-style notebooks with Spark compute for building, training, and deploying machine learning models. Integrates with MLflow for experiment tracking and model registry. Models can be trained on data in OneLake lakehouses and deployed as prediction endpoints.
Key advantage: Data scientists work on the same data that data engineers curate and analysts report on. There is no data copying or separate storage. Model outputs (predictions, scores) land directly in OneLake where Power BI can surface them in dashboards.
Who uses it: Data scientists and ML engineers building predictive models, classification systems, and recommendation engines on enterprise data.
5. Real-Time Intelligence
What it does: Handles streaming and real-time analytics use cases. Includes Eventstreams (for ingesting streaming data from Kafka, Event Hubs, IoT Hub, and custom applications), KQL databases (based on Azure Data Explorer), and real-time dashboards. Data flows in continuously and can be queried in near-real-time.
Key advantage: Before Fabric, real-time analytics required provisioning Azure Data Explorer clusters or configuring Azure Stream Analytics jobs. Fabric makes this serverless — you create an Eventstream, point it at a KQL database, and start querying within minutes.
Who uses it: Operations teams monitoring IoT sensor data, security teams analyzing log streams, and marketing teams tracking real-time campaign performance.
6. Power BI
What it does: The business intelligence and reporting workload. Power BI in Fabric includes everything from Power BI Premium — report authoring, semantic models (datasets), dashboards, paginated reports, and the Power BI mobile experience. The major Fabric-specific enhancement is DirectLake mode.
DirectLake explained: Traditional Power BI operates in either Import mode (data is copied into the Power BI model, limited by memory) or DirectQuery mode (queries are sent to the source, slower performance). DirectLake is a third mode exclusive to Fabric that reads Delta Parquet files directly from OneLake into the VertiPaq engine without importing or querying the source. This gives you Import-level performance with DirectQuery-level freshness — no scheduled refreshes needed.
Who uses it: Every business user who consumes analytics, plus BI developers who build reports and semantic models. This is the workload most organizations start with because it delivers the most visible business value.
7. Data Activator
What it does: A no-code experience for defining triggers and automated actions based on data conditions. You define rules like "when daily revenue drops below $50,000, send a Teams notification to the finance team" or "when inventory falls below threshold, trigger a Power Automate flow to create a purchase order."
Key advantage: Data Activator turns your analytics from passive (people look at dashboards) to active (the system notifies people when action is needed). This is a significant shift for organizations that rely on manual dashboard monitoring.
Who uses it: Business operations teams, supply chain managers, and any role where timely action on data conditions drives business outcomes.
4. OneLake: The Foundation of Microsoft Fabric
If Fabric has one defining innovation, it is OneLake. Every other capability — the workloads, the security model, the pricing simplification — flows from the decision to store all data in a single, shared data lake.
What OneLake Is
OneLake is a single, organization-wide data lake that is automatically provisioned with every Fabric tenant. Think of it as the "OneDrive for data" — just as every Microsoft 365 tenant gets one OneDrive for file storage, every Fabric tenant gets one OneLake for analytics data. There is no storage account to create, no access keys to manage, and no networking to configure.
Single Copy of Data, Open Format
All data in OneLake is stored in Delta Parquet format, which is an open standard. This is critical for three reasons. First, any tool that reads Parquet can access your data — not just Microsoft tools. Second, Delta format provides ACID transactions, schema enforcement, and time travel (query data as it existed at a previous point in time). Third, because every Fabric workload reads from the same OneLake copy, there is no data duplication. Your data engineering team, your SQL analysts, your data scientists, and your Power BI developers all work on the same data.
Shortcuts: Virtual Data Access
OneLake shortcuts are virtual pointers to data stored outside of Fabric. You can create shortcuts to Amazon S3 buckets, Google Cloud Storage, Azure Data Lake Storage Gen2, on-premises HDFS, and other OneLake locations. Shortcuts appear in your lakehouse as if the data were local, but no data is copied. This is particularly valuable for enterprises with multi-cloud architectures — you can query S3 data from Fabric Spark notebooks without moving it.
OneLake Storage Pricing
OneLake storage is billed separately from Fabric capacity at approximately $0.023 per GB per month for standard (hot) storage. There is no charge for data egress within the same region. This is comparable to Azure Data Lake Storage Gen2 pricing but without the complexity of managing storage accounts, access tiers, or lifecycle policies. For most organizations, OneLake storage costs are a small fraction of total Fabric spend — typically 5-10% of the overall bill.
5. Microsoft Fabric vs. Azure Synapse vs. Databricks
This is the question I get asked most often in data analytics consulting engagements. Here is an honest, side-by-side comparison based on my experience deploying all three platforms in enterprise environments.
| Feature | Microsoft Fabric | Azure Synapse Analytics | Databricks |
|---|---|---|---|
| Deployment Model | SaaS (fully managed) | PaaS (you manage resources) | PaaS (runs on your cloud) |
| Data Storage | OneLake (automatic, unified) | ADLS Gen2 (you provision) | Unity Catalog + cloud storage |
| Data Format | Delta Parquet (default) | Various (Parquet, CSV, Delta) | Delta Lake (proprietary extensions) |
| SQL Warehouse | Serverless T-SQL | Dedicated SQL pool (DWU-based) | Databricks SQL (DBU-based) |
| Spark Engine | Managed Spark (auto-scaling) | Synapse Spark (you manage pools) | Photon-optimized Spark |
| BI Integration | Power BI native (DirectLake) | Power BI (DirectQuery only) | Partner BI tools (no native BI) |
| Real-Time Analytics | Built-in (Eventstreams, KQL) | Requires Azure Data Explorer | Structured Streaming |
| Data Governance | Microsoft Purview integration | Microsoft Purview integration | Unity Catalog |
| Pricing Model | Capacity Units (CUs) | Per-resource (DWUs, Spark nodes) | Databricks Units (DBUs) + cloud |
| Best For | Microsoft-centric organizations wanting simplicity | Existing Synapse users with complex configurations | Advanced ML/AI workloads, multi-cloud |
My recommendation: For organizations already invested in the Microsoft ecosystem with Power BI, Microsoft 365, and Azure Active Directory, Fabric is the clear choice. You get native Power BI integration (including DirectLake), unified security through Entra ID, and a simpler billing model. Databricks is the stronger choice if your primary workload is advanced machine learning at scale or if you operate across multiple cloud providers (AWS, Azure, GCP). Azure Synapse is in maintenance mode — I do not recommend starting new projects on it.
For a detailed pricing comparison between Power BI tiers within Fabric, see our Power BI Pricing and Licensing Guide 2026.
6. Microsoft Fabric Pricing Deep Dive
Fabric pricing is based on Capacity Units (CUs), which are a shared compute resource pool. Every Fabric workload — Spark notebooks, SQL warehouse queries, pipeline runs, Power BI report renders — consumes CUs from the same capacity. This is fundamentally different from Azure Synapse, where each service had its own pricing meter.
| SKU | Capacity Units | Pay-As-You-Go | 1-Year Reserved | Power BI Equivalent | Notes |
|---|---|---|---|---|---|
| F2 | 2 CUs | $262.80/mo | ~$199/mo | None | Entry level, dev/test only |
| F4 | 4 CUs | $525.60/mo | ~$398/mo | None | Small team workloads |
| F8 | 8 CUs | $1,051.20/mo | ~$796/mo | None | Small production workloads |
| F16 | 16 CUs | $2,102.40/mo | ~$1,592/mo | None | Mid-size production |
| F32 | 32 CUs | $4,204.80/mo | ~$3,184/mo | None | Large production |
| F64 | 64 CUs | $5,258.88/mo | ~$3,984/mo | = P1 Premium | Minimum for XMLA, unlimited viewers |
| F128 | 128 CUs | $10,517.76/mo | ~$7,968/mo | = P2 Premium | Enterprise workloads |
| F256 | 256 CUs | $21,035.52/mo | ~$15,936/mo | = P3 Premium | Large enterprise |
| F512–F2048 | 512–2048 CUs | $42K–$168K/mo | ~$32K–$128K/mo | = P4–P5 | Fortune 500, heavy compute |
How Capacity Units Map to Power BI Premium
Microsoft aligned Fabric F-SKUs with the legacy Power BI Premium P-SKUs. An F64 provides the same capabilities as a P1 Premium capacity, including unlimited Power BI viewers (users with free licenses can view content), XMLA endpoint access, deployment pipelines, and paginated reports. If your organization is currently paying for Power BI Premium P1 ($4,995/month list), the equivalent F64 at $5,258.88/month on pay-as-you-go or ~$3,984/month with a one-year reservation gives you all the same Power BI capabilities plus access to every other Fabric workload.
Cost Optimization Tips
- Use reservations: One-year reserved instances save 20-25% compared to pay-as-you-go pricing.
- Right-size capacity: Start with F8 or F16 for development and scale to F64 for production. Fabric allows you to scale up or down without downtime.
- Separate dev and prod: Use a smaller F-SKU for development and a larger one for production workloads. This prevents dev Spark jobs from consuming production Power BI capacity.
- Monitor utilization: Use the Fabric Capacity Metrics app to identify underutilized or over-provisioned capacities.
- Pause when idle: F-SKUs can be paused programmatically when not in use, unlike the legacy P-SKUs. This is ideal for dev/test capacities used only during business hours.
Need help right-sizing your Fabric capacity? Our Microsoft Fabric consulting team runs capacity assessments that model your workload patterns and recommend the optimal SKU.
7. Who Should Use Microsoft Fabric?
Fabric is not for every organization. Based on the implementations I have led, here are the scenarios where Fabric delivers the most value — and the scenarios where you should wait.
Fabric Is a Strong Fit If You:
- Already use Power BI Premium: You get Fabric at no additional cost. Activating Fabric workloads on your existing P-SKU capacity is a zero-risk decision.
- Run multiple Azure analytics services: If you manage Azure Synapse, Data Factory, Data Lake Storage, and Power BI separately, Fabric consolidates them with 30-50% lower TCO.
- Need to modernize a legacy data warehouse: Organizations migrating from on-premises SQL Server data warehouses or aging Azure SQL Data Warehouse instances benefit from Fabric's serverless T-SQL warehouse.
- Want a lakehouse architecture: Fabric's lakehouse with Delta Parquet and dual Spark/SQL access is a production-ready implementation of the lakehouse pattern.
- Operate in regulated industries: Fabric inherits Microsoft's compliance certifications (HIPAA, SOC 2, FedRAMP, GDPR), making it suitable for healthcare, finance, and government deployments.
- Have a Microsoft-centric technology stack: If your organization runs Microsoft 365, Azure Active Directory (Entra ID), and Power BI, Fabric integrates natively with your existing security and governance infrastructure.
Fabric May Not Be the Right Fit If You:
- Are a small team using Power BI Pro: If you have fewer than 50 users and only need basic Power BI sharing, the F2 or F4 capacity costs may not be justified. Stay with Pro at $10/user/month.
- Run heavily on AWS or GCP: While Fabric can connect to multi-cloud data via shortcuts, the platform is Azure-native. If your primary cloud is AWS and you need a lakehouse, Databricks on AWS may be a more natural fit.
- Have advanced ML/AI as your primary workload: Databricks offers a more mature ML platform with Photon-optimized Spark, AutoML, and a broader ecosystem of ML tools. Fabric's data science workload is growing but is not yet at parity.
- Need multi-cloud data mesh governance: Databricks Unity Catalog supports AWS, Azure, and GCP from a single governance plane. Fabric's governance is Azure/Microsoft-centric.
8. How to Get Started with Microsoft Fabric
Here is the step-by-step process I follow when onboarding enterprise clients to Fabric. This approach minimizes risk and delivers value within the first 30 days.
Activate a Fabric Trial or Capacity
Go to the Power BI admin portal (app.fabric.microsoft.com) and activate a Fabric trial. This gives you a trial capacity equivalent to F64 for 60 days. If you have Power BI Premium P1 or higher, Fabric is already available — your admin just needs to enable it in tenant settings. For production, purchase an F-SKU through the Azure portal or Microsoft 365 admin center.
Create a Fabric Workspace
Create a new workspace and assign it to your Fabric capacity. This workspace will contain all your Fabric items (lakehouses, warehouses, notebooks, pipelines, reports). I recommend creating separate workspaces for development, staging, and production to match your deployment pipeline.
Build Your First Lakehouse
Create a Lakehouse item in your workspace. This automatically provisions a OneLake storage location with a Files section (for raw files) and a Tables section (for Delta tables). Upload a sample dataset or create a shortcut to your existing data source. Once data is in the lakehouse, it is immediately queryable via both Spark notebooks and the auto-generated SQL analytics endpoint.
Connect Power BI with DirectLake
Create a Power BI semantic model (dataset) that uses DirectLake mode to read directly from your lakehouse Delta tables. Open Power BI Desktop, connect to the lakehouse SQL analytics endpoint, build your data model, and publish to the Fabric workspace. DirectLake gives you Import-level query performance without scheduled refreshes — reports reflect the latest data in OneLake automatically.
Build a Data Pipeline
Use Data Factory to create a pipeline that ingests data from your source systems (SQL Server, Salesforce, SAP, flat files) into your lakehouse on a schedule. If you have existing Azure Data Factory pipelines, you can migrate them to Fabric with minimal changes. Set up orchestration to run Spark notebooks for transformation after ingestion.
Implement Governance and Security
Configure workspace roles (Admin, Member, Contributor, Viewer), enable sensitivity labels through Microsoft Purview, and set up row-level security on your semantic models. For regulated industries, enable audit logging and configure data loss prevention policies. Fabric inherits your Entra ID (Azure AD) security model, so existing user groups and conditional access policies apply automatically.
Scale to Production
Once your proof of concept is validated, right-size your Fabric capacity based on actual utilization data from the Capacity Metrics app. Set up deployment pipelines (dev, test, prod) for managed releases. Migrate additional workloads from legacy services. Most of our enterprise clients complete this cycle in 60-90 days.
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9. Fabric + Power BI: The DirectLake Advantage
The integration between Fabric and Power BI is the single most compelling reason for Microsoft-centric organizations to adopt Fabric. Here is why.
The Problem with Traditional Power BI Data Access
Before Fabric, Power BI operated in two modes: Import (copy data into the Power BI model, scheduled refresh, limited by memory) and DirectQuery (query the source live, no data copied, but slower performance and source load). Import gave fast queries but stale data between refreshes. DirectQuery gave fresh data but at a significant performance cost. Neither was ideal for large-scale enterprise analytics.
DirectLake: The Best of Both Worlds
DirectLake is a third data access mode exclusive to Fabric. It reads Delta Parquet files directly from OneLake into the Power BI VertiPaq in-memory engine — but without a scheduled import. When the underlying lakehouse data changes, DirectLake detects the new files and loads them on the next query. You get Import-level performance (sub-second query response) with DirectQuery-level freshness (no scheduled refresh needed).
What This Means in Practice
- No more refresh failures: DirectLake eliminates the scheduled refresh entirely. Data engineers update the lakehouse, and Power BI reports reflect the changes within minutes.
- Larger datasets: Import mode models are limited by capacity memory (25 GB for P1). DirectLake models can handle much larger datasets because data is read on-demand from OneLake rather than fully loaded into memory.
- Reduced capacity load: Import refreshes consume significant capacity. DirectLake shifts that cost to on-demand column loading, which is typically lighter on capacity utilization.
- Simpler architecture: No more designing complex incremental refresh strategies or partitioning models to work around Import limitations.
Semantic Models in Fabric
Power BI semantic models (formerly datasets) in Fabric work the same as they do in Power BI Premium. You define measures in DAX, configure row-level security, set up calculation groups, and publish to the service. The difference is that your semantic model sits alongside the lakehouse and warehouse in the same workspace, with the same security and governance model. XMLA endpoints are available on F64 and above for third-party tool connectivity (Tabular Editor, DAX Studio, Excel).
For organizations evaluating Fabric specifically for Power BI improvements, I recommend reading our Power BI vs. Tableau enterprise comparison for additional context on how Fabric strengthens Power BI's position in the BI market.
Frequently Asked Questions About Microsoft Fabric
What is Microsoft Fabric in simple terms?
Microsoft Fabric is a single cloud platform that combines everything an organization needs for data analytics into one product. Instead of buying and connecting separate tools for data storage, data engineering, data warehousing, real-time analytics, data science, and business intelligence, Fabric bundles all of these capabilities into a unified SaaS experience. All data is stored once in a shared data lake called OneLake, and every workload reads from that single copy. Think of it as Microsoft consolidating Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage, Power BI Premium, and several other Azure services into one integrated platform with one billing model.
Is Microsoft Fabric free?
Microsoft Fabric offers a free trial that provides a limited Fabric capacity for 60 days, which is sufficient to explore the platform and build proof-of-concept projects. After the trial, Fabric requires a paid capacity subscription. The smallest paid capacity is the F2 SKU at approximately $262.80 per month on a pay-as-you-go basis. Organizations with existing Power BI Premium P1 or higher subscriptions can access Fabric workloads at no additional cost, as Microsoft converted P-SKUs to include Fabric capacity in late 2024. Power BI Pro users ($10/user/month) can create Fabric items in trial capacity but cannot run production workloads without a paid Fabric capacity.
What is the difference between Microsoft Fabric and Azure Synapse?
Azure Synapse Analytics is an IaaS/PaaS service that requires you to provision and manage individual Azure resources such as dedicated SQL pools, Spark pools, and linked services. Microsoft Fabric is a fully managed SaaS platform that bundles equivalent capabilities (data engineering, warehousing, Spark, pipelines, real-time analytics) into a single product with shared OneLake storage. Fabric eliminates the need to configure networking, manage Spark clusters, or integrate separate storage accounts. Microsoft has stated that Fabric is the future of their analytics platform, and while Synapse is not being deprecated immediately, new features and innovation are being directed toward Fabric. For new projects, Fabric is the recommended starting point.
Do I need Microsoft Fabric if I already have Power BI Premium?
If your organization already has Power BI Premium (P1 or higher), you already have access to Microsoft Fabric. Microsoft converted all Premium capacities to include Fabric workloads starting in late 2024. This means you can use Data Engineering, Data Warehouse, Data Factory, Data Science, and Real-Time Intelligence workloads within your existing Premium capacity without purchasing additional licenses. However, Fabric workloads share the same capacity units (CUs) as your Power BI workloads, so running heavy Fabric jobs may impact Power BI report performance. Many organizations choose to purchase a separate Fabric capacity (F-SKU) dedicated to data engineering and warehousing to avoid contention with Power BI.
What is OneLake in Microsoft Fabric?
OneLake is the unified data lake that underpins all of Microsoft Fabric. It is a single, organization-wide storage layer built on Azure Data Lake Storage Gen2. Every Fabric workspace automatically gets a OneLake storage location, and all Fabric workloads (data engineering, warehousing, data science, Power BI) read from and write to OneLake. Data is stored in open Delta Parquet format, which means it can be accessed by non-Microsoft tools as well. OneLake supports shortcuts, which are virtual pointers to data stored in external locations such as Amazon S3, Google Cloud Storage, or on-premises HDFS. This eliminates the need to copy data into Fabric before you can query it.
How much does Microsoft Fabric cost?
Microsoft Fabric pricing is based on capacity units (CUs). The smallest SKU is F2 at $262.80/month (pay-as-you-go) or approximately $199/month with a one-year reservation. F4 costs $525.60/month, F8 costs $1,051.20/month, F16 costs $2,102.40/month, F32 costs $4,204.80/month, and F64 costs $5,258.88/month. The F64 SKU is the minimum required for Power BI features like XMLA endpoints and unlimited viewer access. Larger SKUs (F128 through F2048) are available for enterprises with heavy compute needs. OneLake storage is billed separately at approximately $0.023 per GB per month for standard storage. Organizations can also use Azure Reserved Instances to save 20-40% on Fabric capacity.
Can I use Microsoft Fabric without Azure?
Microsoft Fabric is a SaaS product that runs on Azure infrastructure, but you do not need a separate Azure subscription to use it. Fabric capacity can be purchased directly through the Microsoft 365 admin center or the Power BI admin portal using F-SKU licensing. OneLake storage is included with and managed by Fabric. However, if you want to use Fabric shortcuts to connect to data in Azure Blob Storage, Azure SQL, or other Azure services, you will need those Azure resources provisioned in an Azure subscription. For organizations that are entirely outside of Azure, Fabric can also use shortcuts to connect to Amazon S3 and Google Cloud Storage.
What certifications cover Microsoft Fabric?
Microsoft introduced the DP-600: Implementing Analytics Solutions Using Microsoft Fabric certification in 2024. This is currently the primary certification that validates Fabric expertise. It covers OneLake, lakehouses, data warehousing, data engineering with Spark, data pipelines, real-time analytics, and Power BI integration within Fabric. The existing DP-500: Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI certification also overlaps with Fabric concepts. Microsoft has signaled that the DP-600 will become the standard analytics certification going forward. For Power BI-specific skills, the PL-300: Microsoft Power BI Data Analyst certification remains relevant and is often pursued alongside DP-600.
Need Help with Microsoft Fabric?
Whether you are evaluating Fabric for the first time, migrating from Azure Synapse, or optimizing an existing Fabric deployment, our team has the enterprise experience to get it done right. We have architected Fabric implementations for Fortune 500 organizations across healthcare, finance, and government — the compliance-heavy industries where mistakes are not an option.
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