Choosing Between Microsoft Fabric and Databricks: A Guide for Your Data Analytics Needs

Are you struggling to decide between Microsoft Fabric and Databricks for your data analytics needs? You're not alone! Both are powerful cloud-based platforms, but which one is the perfect fit? Don't worry, we're here to help! We'll break down their features, functionalities, and how they can benefit your organization, making the decision a breeze.

Fabric vs Databricks

ūüĎČ Understanding the Landscape

Imagine a one-stop shop for all your data needs ‚Äď that's Microsoft Fabric. This unified platform integrates data engineering, data science, machine learning, and business intelligence tools, all within a single ecosystem. Plus, it offers a user-friendly, no-code/low-code experience, so even beginners can jump right in.

On the other hand, Databricks is a powerhouse built for data professionals. This cloud-based platform leverages Apache Spark for serious processing muscle. It fosters collaboration among data scientists, engineers, and analysts, but keep in mind, it requires coding expertise. While Databricks itself can work on Azure, AWS, or GCP, here, we'll focus on how it compares to Azure Databricks specifically.

Now, let's dive deeper and see how these two platforms stack up against each other.

High-level comparison

Consideration Microsoft Fabric Databricks

Deployment Model

SaaS (Software as a Service) - Managed by Microsoft

PaaS (Platform as a Service) - fine-grained control over infrastructure


Infrastructure Setup

No configuration required

Requires Infrastructure as Code (IaC) setup for customization


Data Location Control

Limited control (data resides in your OneLake, which is linked to your Fabric Tenant)

More control over data residency and network isolation



Delta format, Spark Engine & cluster-based

Similar core architecture, but Databricks offers more configuration options


Data Warehouse

Offers native TSQL & stored procedures compatibility, but also PySpark & Spark SQL

Relies on PySpark & Spark SQL


Development Environments

Distinction between environments is handled by creating different workspaces

Full support for separate DTAP environments


Data Catalog & Governance

Purview (still in preview) - can be a joined venture with Unity Catalog

Unity Catalog


CI/CD Compatibility

Limited support (Preview features) & limited branching support

Full compatibility with CI/CD pipelines with Git & DevOps


Business Intelligence Integration
(Power BI)

Connection possible with Import & Direct Query & Direct Lake for optimized performance

Connection possible with Import & Direct Query with cluster or SQL warehouse


Data Sharing

Fabric API offers some sharing but is still limited (preview features)

Delta Sharing & Databricks API


Data Ingestion

Fabric Data Factory for (Low) Code & Dataflow Gen 2 for No-Code & Full code possible in Lakehouse

Full code in Databricks or (Low)-Code via Azure Data Factory


Data Transformation

Low-code with Dataflow Gen 2 & Lakehouse for Spark-based transformations & Warehouses for SQL-based Transformation

PySpark or Spark SQL transformations in Notebooks & Delta Live Tables


Access Control

Very basic currently, as OneSecurity is not available yet

Mature & comprehensive suite of security features with Unity Catalog


Advanced Analytics (Machine Learning & Streaming)


Supported - Native integration with MLflow


AI Assistant

CoPilot is available in each step of your data warehouse journey

Available as a code helper in notebooks and in the SQL editor


Overall Maturity

Less mature but rapidly evolving

More mature & established platform (10+ years of evolution)

ūüĎČ Key takeaways

Deployment Model & Infrastructure: 

  • Microsoft Fabric: Easier setup, but customization might be required for on-premises data sources or private endpoints. Fabric offers convenience, while Databricks provides more fine-grained control.¬†
  • Databricks: This requires manual setup and infrastructure management (IaC is recommended). You'll need to configure additional components for your data platform, such as storage and networking.¬†

Architecture & Data Warehousing: Both platforms leverage Delta Lake architecture. 

  • Microsoft Fabric: Streamlines legacy migrations with built-in TSQL and stored procedure support in its Warehouse component.¬†
  • Databricks: Requires alternative approaches for migrating legacy data warehouses, such as rewriting code in Spark SQL.


  • Microsoft Fabric: CI/CD functionality is under development and not yet mature.¬†
  • Databricks: Fully compatible with DevOps tools and Git for seamless integration into your development workflow.¬†

Data Ingestion & Transformation:

  • Microsoft Fabric:¬†Offers a no-code/low-code alternative with Dataflow Gen2 for data ingestion and transformation, making it easier for users with limited coding experience. Additionally, users can leverage notebooks for transformations in the Lakehouse and stored procedures in the Warehouse. For more advanced data orchestration and ETL capabilities, Data Factory can be used.
  • Databricks: Primarily relies on code-based data ingestion and transformation through Databricks notebooks. Additional tools like Azure Data Factory might be necessary for complex workflows.¬†


  • Microsoft Fabric: Security features are evolving. While workspace security and access control exist, advanced features like Row-Level Security (RLS), Object-Level Security (OLS), and dynamic data masking are currently limited to the Warehouse component. Using these features disables Direct Lake and defaults to Direct Query in Power BI, impacting performance. The future integration of OneSecurity with Fabric promises significant security improvements.¬†
  • Databricks: Provides robust security with granular control through Unity Catalog rules. These rules can be applied to Power BI with Direct Query, but performance might be affected.¬†

For optimal performance with robust RLS rules in Power BI reports, using an import connection is currently recommended. 

ūüĎČ Making the Final Call

So, Microsoft Fabric or Databricks? Here's a cheat sheet to help you pick your winner:

Team Microsoft Fabric

  • New to Spark? No problem! Fabric's low-code/no-code options are perfect for beginners. Think of it as training wheels for your data game.¬†
  • Migrating from a familiar friend? If you're moving from an SQL-based data warehouse, Fabric speaks your language with native TSQL & stored procedure compatibility.¬†
  • Just want things to work? Fabric prioritizes ease of use and minimal maintenance. Everything is taken care of.
  • Need results fast? Direct Lake allows for near-real-time reporting, keeping your finger on the pulse of your business.¬†
  • Embrace the update! Fabric is constantly evolving with new features, so the platform will mature at a deliberate pace. Be prepared to adapt as things change.

Team Databricks

  • Got a data dream team? Databricks is built for collaboration among seasoned data professionals.¬†
  • Ready to push the boundaries? For complex data problems, Databricks offers the muscle you need.¬†
  • Control freak (in a good way)? Databricks gives you granular control over your infrastructure and data residency. It's your data, your way.¬†
  • Like a well-oiled machine? Databricks integrates with advanced development features like CI/CD and separate DTAP environments, streamlining your workflow. The Databricks team have a track record of implementing and delivering new features...

The Verdict

There's no one-size-fits-all answer in the battle between Microsoft Fabric and Databricks. The best platform depends on your unique data squad, project goals, and budget.

The decision between Databricks and Microsoft Fabric hinges on various factors. Databricks is currently more mature and established in the market, offering a robust and proven platform. However, Microsoft Fabric is rapidly evolving and catching up, showing great potential for the future. Your team's familiarity and expertise with either platform will also play a crucial role in making the right choice.

Think of Microsoft Fabric as the user-friendly, all-in-one solution for businesses that are new to data exploration or want to stay on the cutting edge. Databricks, on the other hand, is the coding powerhouse for experienced data teams tackling complex problems.

This is just the first step. Do your research, consider your specific needs, and pick the champion that will help you unlock the power of your data!