Exploring the Modern Data Landscape: Microsoft Fabric vs. Databricks

Our multidisciplinary approach ensures a seamless integration that optimizes performance, allowing you to unlock the full potential of your data assets. Implementing Contata’s data solutions based on Microsoft Fabric or Azure brings substantial benefits to businesses.

Category: Data Science
By Contata Published on: January 30, 2024

In the dynamic landscape of data, organizations face the ever-growing challenge of transforming large volumes of raw information into actionable insights. The ability to derive meaningful insights from this wealth of information not only propels informed decision-making but also fuels innovation, making data platforms a vital choice for organizations striving to gain a competitive edge. Due to their ability to seamlessly integrate structured and unstructured data, modern data lakehouses are gaining popularity over traditional data warehouses.

Traditional Data Warehouse to Modern Data Lakehouse – Why the Shift?

Unlike data warehouses, lakehouses can handle diverse data types and formats, providing more flexibility to organizations for big data analytics. Additionally, lakehouses leverage scalable cloud storage and processing, allowing for cost-effective scalability based on business needs. This adaptability, combined with the ability to process data in its raw form, makes data lakehouses well-suited for the dynamic and diverse data landscape of today’s enterprises.

Microsoft Fabric vs. Databricks: What They Are

Microsoft Fabric and Databricks have emerged as prominent players in the modern data space, each offering a unique set of features tailored to address the multifaceted challenges of data processing. While Databricks has been in use for nearly a decade, Fabric has been introduced recently. Let’s discuss what value these two platforms bring to businesses in their own capacities.


Launched in 2013, Databricks is a popular cloud-based data platform built on top of Apache Spark that is used for building, deploying, sharing, and maintaining enterprise-grade data, and can be integrated with all major cloud providers, including AWS, Azure, and Google Cloud Platform.

Microsoft Fabric

Launched in May 2023, Microsoft Fabric is an “all-in-one” analytics solution built on top of Azure Synapse Analytics and Azure Data Factory (ADF). The platform integrates everything from data engineering to data processing, real-time analytics, and business intelligence (BI)—all in one place.

Microsoft Fabric vs. Databricks: Key Differences

  • Architectural Differences – While Databricks’ architecture consists of various platforms and relies on integrations to provide a unified platform, Microsoft Fabric bundles together different Azure technologies and a host of other technologies to offer a more seamless experience, preventing the need to piece together different services from multiple vendors.
  • Integration Capabilities – In addition to AWS, Azure, and Google Cloud Platform, Databricks’ integrated solutions include Apache Spark, Databricks Notebooks, MLFlow, Delta Lake, and more. Fabric’s bundled offerings include Azure Synapse Analytics, Azure Data Factory, Power BI, Azure Databricks, Azure Machine Learning OneLake, Microsoft’s AI assistant, CoPilot, etc.
  • Set Up Prerequisites – Setting up Microsoft Fabric requires a user to have at least one of the following admin roles: Microsoft 365 Global admin, Power Platform admin, or Fabric admin. Enabling Databricks requires an active Azure or AWS subscription.

Microsoft Fabric vs. Databricks: Pricing Model

Microsoft Fabric has two pricing models: Pay-as-you-go and Reservation. The cost of using the platform is based on the number of capacity units (CUs) a business is planning to use, which ranges from $0.36/hour (on the pay-as-you-go model) and $0.215/hour (under the reservation model) for 2 CUs to $368.64/hour (on the pay-as-you-go model) and $219.295/hour (under the reservation model) for 2048 CUs.

Databricks has a relatively complex pricing model as compared to Microsoft Fabric. The cost is based on a variety of factors like the number of virtual machines utilized, runtime hours, and data storage usage. Here’s how the pricing works in Databricks.

  • Workflows & Streaming – $0.07/DBU for data engineering and data lake management.
  • Delta Live Tables: $0.20/DBU for ETL pipelines.
  • Databricks SQL: $0.22/DBU for BI and analytics.
  • All Purpose Compute: $0.40/DBU for data science and ML.
  • Serverless Real-time Inference: $0.07/DBU for live predictions.

Note: Databricks offers a 14-days free trial.

Integrating Microsoft Fabric & Databricks – A Synergistic Solution

By integrating both Databricks and Microsoft Fabric into their data processes, organizations can create a robust and synergistic solution to achieve operational efficiency.

Here is an example of how a manufacturing company can use Databricks and Microsoft Fabric in combination for predictive maintenance.

Step 1:Designing Architecture

The company can leverage Microsoft Fabric to design a scalable microservices architecture, providing flexibility for predictive maintenance.

Step 2:Unified Data Processing and Integration

The company will integrate Databricks into the architecture for unified data processing, ensuring seamless handling of data from sensors, IoT devices, and historical records.

Step 3: Data Exploration, Preparation, and Feature Engineering

The company can utilize Databricks notebooks for comprehensive data exploration, preparation, and feature engineering to ensure high-quality and relevant data.

Step 4 Machine Learning Model Development
The company will leverage Databricks for machine learning model development to predict equipment failures based on historical data and engineered features.

Step 5: Real-Time Integration and Monitoring
The company can integrate trained machine learning (ML) models into Microsoft Fabric for real-time monitoring of equipment health.

Step 6: Continuous Monitoring and Improvement
The company will implement monitoring tools within both Databricks and Microsoft Fabric for continuous evaluation and improvement of the predictive maintenance system.

Step 7: Deployment and Scaling
The company can deploy the integrated solution at scale using Microsoft Fabric, utilizing its auto-scaling features to handle varying workloads effectively.

Step 8: Reporting and Insights
The company will utilize Databricks for detailed analytics and reporting on the effectiveness of the predictive maintenance system, providing actionable insights for optimization.

Implementation in Different Industries: Business Use Case

Microsoft Fabric and Databricks are powerful tools that can be implemented across various industries to meet several data-driven purposes.


Microsoft Fabric: Designing and deploying scalable, resilient, and highly available applications, improving healthcare systems’ efficiency.
Databricks: Processing and analyzing large volumes of healthcare data, aiding in predictive analytics for patient outcomes and resource optimization.

Microsoft Fabric: Enabling the development of secure and reliable financial applications, supporting real-time transaction processing and seamless integration with other financial systems.
Databricks: Data processing and machine learning in finance, enhancing fraud detection, risk assessment, and portfolio optimization.


Microsoft Fabric: Supporting the creation of smart manufacturing applications, facilitating the integration of IoT devices, real-time monitoring, and predictive maintenance.
Databricks: Analyzing manufacturing data to improve production efficiency, quality control, and supply chain management through advanced analytics.


Microsoft Fabric: Building scalable e-commerce platforms, ensuring a seamless shopping experience and integrating with other retail systems.
Databricks: Analyzing customer behavior, optimizes pricing strategies, and enhances inventory management through data-driven insights.


Microsoft Fabric: Facilitating the development of highly available and scalable communication services, ensuring reliable connectivity and seamless user experiences.
Databricks: Analyzing network data to optimize infrastructure, predict network failures, and enhance customer service in telecommunications.

Final Thoughts

Both Microsoft Fabric and Databricks offer top-notch data management capabilities. The choice completely depends on the business requirements and the industry you are in. If seamless integration with Azure services is what you are looking for, Microsoft Fabric is your best bet. On the other hand, if you’re seeking collaborative analytics and optimized Apache Spark performance, Databricks is a better fit.

Contata: Bridging the Gaps in Data with Fabric & Databricks

In the realm of data solutions services, Contata emerges as a crucial player with expertise spanning both Microsoft Fabric and Databricks. Leveraging a deep understanding of data platforms, we craft solutions that align precisely with your organization’s objectives.

Our multidisciplinary approach ensures a seamless integration that optimizes performance, allowing you to unlock the full potential of your data assets. Implementing Contata’s data solutions based on Microsoft Fabric or Azure brings substantial benefits to businesses, including:

  • Seamless integration – Seamless integration ensures optimized performance and scalability, allowing you to effectively harness the power of your data.
  • Technical Expertise – Contata has the expertise to handle data projects of all sizes. We streamline the implementation process, reducing complexities and ensuring a quicker return on investment.
  • Unified Approach – Contata’s unified approach enhances collaboration across teams, fostering a data-driven culture that propels innovation and informed decision-making.

Elevate your analytics and business intelligence capabilities with Contata. Reach out to us today!