Microsoft Fabric vs Databricks: Making the Right Choice

Businesses need platforms that combine analytics, AI, and business intelligence without requiring teams to become integration experts. That’s where suites like Microsoft Fabric and Databricks come in. Both platforms solve similar problems but take completely different approaches.

The tricky part? Their different pricing structures, architectures, and capabilities can make the final decision overwhelming. The platform you choose now will influence your data strategy for years, so getting it right matters.

Understanding the Platforms

What is Microsoft Fabric?

Microsoft Fabric pulls together Power BI, Azure Data Factory, and Synapse Analytics into one unified experience through OneLake – a central data lake where all your information lives.
Fabric is designed for simplicity and end to end analytics from ingestion through to action. If you’re using Azure cloud infrastructure, it integrates seamlessly with built-in low-code options, ideal for non-technical users.

What is Databricks?

Databricks, founded in 2013 by the Apache Spark creators, pioneered “lakehouse architecture”-combining data lakes and data warehouses. Where Fabric is powerful, it can be argued to prioritise simplicity, whilst Databricks focuses on power and flexibility.

Databricks is built for organisations processing massive data volumes, running complex machine learning models, or working across multiple clouds.
The platform gives technical teams serious control through interactive notebooks, MLflow for ML workflows, and distributed computing.

Why This Decision Matters

The data analytics market growth is heading toward $132.9 billion by 2026. Nearly 65% of organisations are now bringing AI into their data analytics work. Your chosen platform needs to handle both today’s business intelligence needs and tomorrow’s AI ambitions.

Key Differences That Matter

Architecture and Integration

The way these platforms are built shapes everything about them.

Microsoft Fabric is fully managed SaaS. Microsoft handles all infrastructure behind the scenes-you just use it. The downside? Less control over resource allocation. Fabric’s big advantage is how it connects with Microsoft 365, Power BI, Teams, and Azure. If you’re in the Microsoft ecosystem, integration is effortless.

Databricks gives you Platform-as-a-Service flexibility. You manage compute clusters and can fine-tune based on workload needs. This is great for technical teams after control, but adds complexity. Databricks takes an open approach with complementary capabilities connecting to tools like Fivetran and Informatica.

Data Processing

Fabric offers multiple compute engines (SQL, Spark, KQL, VertiPaq) through one interface. Data Factory makes it easy for business users to build pipelines without heavy coding. There is an argument Fabric’s processing capabilities are still catching up to Databricks for very heavy workloads.
Databricks built its reputation on Apache Spark optimisation. The platform handles Delta Live Tables for transformation and streaming at serious scale.

Pricing

The pricing model comparison shows completely different approaches.

Fabric uses capacity-based pricing. You buy a capacity size (F8, F16, F32) covering all workloads. The advantage is cost predictability. The challenge? Figuring out capacity needs upfront. There’s no simple formula.

Databricks charges by consumption using Databricks Units (DBUs). You pay for VM time plus DBUs based on workload type, tier, and cloud provider, plus separate storage costs. This offers flexibility but costs depend on your usage patterns.

Which Platform is Right for You?

When Microsoft Fabric Makes Sense

Fabric works when you’re integration and ease of use:

• You’re already deep in Microsoft 365, Power BI, and Azure
• Business intelligence and reporting are your main focus
• You need something business users can use without data engineering teams
• Infrastructure management isn’t your strength
• Your data needs are solid but not extreme

When Databricks is Better

Databricks shines when you need power and flexibility:

• You’re dealing with massive data volumes and complex processing
• AI and machine learning are central to your strategy
• You need multi-cloud flexibility (AWS, Azure, GCP)
• Your team has strong data engineering expertise
• Your workloads vary and you want consumption-based pricing

Can You Use Both?

Yes. Some organisations use Databricks for heavy-duty data engineering and ML model training, then feed processed data into Fabric’s OneLake for business teams using Power BI. This gives you Databricks’ power with Fabric’s user-friendly analytics. The catch? You need solid data management expertise to design this effectively.

What Are the Migration Challenges?

Microsoft Fabric Migration

Microsoft provides migration planning resources and automated migration tools, but challenges remain:

• T-SQL compatibility issues – Not all features from Synapse or SQL Server translate cleanly
• Managed tables approach – Fabric controls both metadata and data files, requiring architecture changes
• Capacity planning difficulty – No formula to estimate needs beforehand; requires trial and measure
• Security reconfiguration – Must use Microsoft Entra ID; permissions need rebuilding

Plan for a phased migration rather than big-bang, and budget extra time.

Databricks Migration

Industry experts document a structured migration framework:

• Code conversion – Translating vendor-specific SQL to Spark SQL is time-intensive
• Data migration strategy – Historical loads, incremental loads, keeping systems in sync
• Performance tuning – Understanding Delta Lake, Z-ordering, clustering takes expertise
• Learning curve – Teams need to understand Spark, Delta Lake, distributed computing

Successful migrations follow five phases: discovery, proof of concept, planning, execution, and knowledge transfer.

Making Your Final Decision

Decision Framework

Work through these questions:
Technology: Which cloud providers? How much Microsoft integration? Current tools?
Team: Technical skill level? Data engineers and scientists? Infrastructure management appetite?
Workloads: Data volumes? Real-time streaming needs? ML intensity?
Business: Primary use case (BI vs ML)? Multi-cloud required? Cost predictability importance?
Future: Data strategy in 3-5 years? Major AI/ML plans? Expected data growth?
Answer these honestly, and the right platform becomes clearer.

Getting Expert Guidance

These platform decisions involve significant investment and long-term commitment. Working with people who’ve done this before makes sense-not vendors, but consultants who understand both platforms and can give objective advice.
The right partner assesses your current setup, understands your business goals, and helps design something that actually works. This is especially important for strategic transformation initiatives. Expert guidance helps avoid cloud migration risks and ensures your platform delivers expected value.

The Path Forward

There’s no universal answer to Microsoft Fabric vs Databricks. Both platforms excel in different scenarios.

Choose Fabric if you’re prioritising integration, simplicity, and business intelligence with existing Microsoft investments.

Choose Databricks if you need serious processing power, flexibility, and advanced capabilities with multi-cloud support.

Your decision should be driven by what you have in place, what your team knows, where you are in your data journey, and where your business strategy is taking you.

QuoStar’s data management expertise means we work with both platforms regularly. We provide objective guidance based on what fits your situation.

Request a Consultation and let’s talk through your specific needs. We’ll help you figure out the right platform strategy that delivers real value-whether that’s Fabric, Databricks, or some combination that makes sense for you.