In this blog, we break down the key differences between Microsoft Fabric and Databricks notebooks— comparing their pricing, features, and capabilities — to help you choose the right platform for your business needs.
In today’s world, data is the backbone of decision-making, innovation, and business growth. With the explosion of big data, companies need powerful platforms that can not only process massive amounts of information but also help visualize and understand it.
Two of the most talked-about platforms in this space are Databricks and Microsoft Fabric. But which one is right for your needs? Let’s dive in and break it down!
Introduction to Databricks and Microsoft Fabric
Databricks is a leading data analytics platform built on top of Apache Spark and Delta Lake. It’s designed for big data processing, machine learning, artificial intelligence, and advanced analytics. Databricks is used by data engineers, scientists, and analysts to handle everything from ETL pipelines to ML model training, with strong support for notebooks and programming languages like Python, Scala, and SQL.
Microsoft Fabric is Microsoft’s next-generation, all-in-one analytics platform. It brings together data engineering, data integration, data science, real-time analytics, and business intelligence — all inside a Software-as-a-Service (SaaS) platform. Fabric is tightly integrated with Power BI, making it a powerful tool for business users and analysts who want to transform, analyze, and visualize data using drag-and-drop interfaces and notebooks.
How Databricks and Microsoft Fabric perform when you need to process 100 million records daily. Let’s compare processing power, cost, and visualization capabilities
Processing Capacity
Databricks
Databricks is designed for high-scale data processing. Handling 100 million records per day is well within its comfort zone. With its Spark-based distributed engine, it can parallelize the workload across many nodes, ensuring fast performance even as data volumes grow.
Best for large-scale pipelines, ETL, ML, and AI workloads.
Microsoft Fabric
Fabric can also handle 100 million records per day, especially when using Dataflows, Pipelines, or Synapse integration. But it’s primarily optimized for data integration + business analytics — not ultra-heavy raw data crunching. As the data size grows toward billions of rows, Fabric may struggle compared to Databricks.
Best for combining data prep + reporting, but not for ultra-massive compute jobs.
Winner on processing power: Databricks — especially if you expect the data to keep growing.
Price (Approximate, Indian Rupees 🇮🇳)
Databricks
- Small-medium cluster: ₹170–₹850 per hour
- Daily processing of 100M records (~1–2 hours/day) → ~₹170–₹1,700 per day → ~₹5,100–₹51,000 per month
- Pay-as-you-go + storage costs
Microsoft Fabric
- F2 capacity: ₹25,000–₹35,000 per month (handles multiple pipelines + dashboards)
- Includes both compute + Power BI integration
- More predictable pricing if you have fixed workloads.
Winner on price (for predictable cost + dashboards): Microsoft Fabric. But if you want fine-tuned, cost-efficient scaling for raw compute, Databricks can save money on smaller workloads.
Visualization in Notebooks
Databricks
Provides built-in notebook visualizations like tables, charts, plots, and maps, but often needs external tools (Power BI, Tableau, Looker) for advanced dashboards. Best suited for data engineers, data scientists, and technical teams.
Microsoft Fabric
Deeply integrated with Power BI, offering drag-and-drop dashboard creation right from the Fabric workspace and notebooks. Great for business users, analysts, and non-technical teams.
Winner of visualization experience: Microsoft Fabric
AI / Machine Learning Support
Databricks
Excellent ML support; has MLflow, AutoML, and deep integration with popular ML/DL frameworks.
Microsoft Fabric
ML integration exists (through Azure ML), but it’s not as tightly embedded as in Databricks.
Winner (AI/ML workloads): Databricks
Security & Governance
Databricks
Strong enterprise-grade security, Unity Catalog, RBAC, encryption, and compliance tools.
Microsoft Fabric
Built on Microsoft’s enterprise security foundation, with role-based access, data lineage, and sensitivity labels.
Winner: Tie — both are strong in this area.
Conclusion
Both Databricks and Microsoft Fabric are powerful platforms, but they shine in different areas.
If your priority is large-scale data processing, machine learning, and cloud flexibility, Databricks is the clear winner — especially when working with trillions of records or building advanced analytics pipelines.
On the other hand, if you want a business-friendly platform with built-in visualization, seamless integration with Power BI, and predictable pricing, Microsoft Fabric is an excellent choice — particularly for teams already invested in the Microsoft ecosystem.
The right choice depends on your use case, team skills, and business goals:
- For data engineers and scientists → Databricks
- For business analysts and BI teams → Microsoft Fabric
Whichever platform you choose, both can help transform your data into insights — it’s all about picking the right tool for your journey.