Microsoft Fabric Vs Databricks: A Comparison Guide

12 min readOct 31, 2023

Explore the key differences between Microsoft Fabric vs Databricks in terms of pricing, features, and capabilities, and choose the right tool for your business.

Microsoft Fabric Vs Databricks: A Comparison Guide

When the creators of Apache Spark formed Databricks in 2013, they utilized a market gap and created a lakehouse architecture that transformed data analytics for enterprises. 10 years later, Microsoft has attempted to do the same with Microsoft Fabric — create a robust data management and analytics solution that is easy to access and collaborate with.

So between Microsoft Fabric vs Databricks, what should modern businesses choose in 2023? The old but reliable Databricks, or the new and exciting Microsoft Fabric?

Let’s dive in and compare the essential aspects of these data platforms. We present the comprehensive Microsoft Fabric vs Databricks guide.The Contenders: Microsoft Fabric and Databricks

What is Microsoft Fabric?

Microsoft Fabric is an all-in-one analytics platform launched in May 2023. It provides a unified environment for data engineering, data science, machine learning, and business intelligence.

Fabric is built on top of Azure Synapse Analytics and Azure Data Factory. It includes a variety of other services, from Azure Data Fabric architecture e.g. Power BI, Azure Databricks, and Azure Machine Learning.

Source: Microsoft

What is Databricks?

Databricks is a unified analytics platform, built on top of Apache Spark. It provides a variety of features for data processing, data warehousing, and machine learning. It was founded in 2013.

Databricks is a cloud-based platform and is available on all major cloud providers, including AWS, Azure, and Google Cloud Platform.

Its comprehensive set of features, from optimized Spark performance to collaborative workspaces, makes it an invaluable tool.

Microsoft Fabric vs Databricks: Architecture

Fabric Architecture and Benefits

Microsoft Fabric bundles together different Azure technologies on top of its OneLake system, and bundles it all up with additional features such as Microsoft’s AI assistant, CoPilot and a host of other technologies that aim to increase productivity and awareness within different teams.

Source: Microsoft

Microservices Architecture: Microsoft Fabric is designed from the ground up to support microservices patterns. This architecture allows developers to build applications as small, independent services that can be developed, and scaled individually.
Container Orchestration: With the rise of containerization, Azure Data Fabric architecture provides built-in support for orchestrating containers. The feature allows developers to deploy and manage both Windows and Linux containers.
Stateful Services: Unlike some other platforms that only support stateless services, Microsoft Fabric architecture supports stateful services. This means that the platform can maintain the user sessions or events without relying on external databases or caches.
Scalability and Load Balancing: The platform is designed to handle large-scale applications. It can automatically balance loads, ensuring that each service instance gets its fair share of requests. As demand grows, Microsoft Fabric can scale out the necessary services to meet the increased load.
Rolling Upgrades and Rollbacks: Deploying updates and new features is a breeze with Microsoft Fabric architecture. It supports rolling upgrades, meaning that new versions of a service can be deployed without downtime. If something goes wrong, it also supports automatic rollbacks to the previous stable version.

Databricks Architecture and Benefits

Databricks’ architecture consists of various platforms and integrations that work together to provide a unified workspace. Here they are, along with the benefits:

Unified Analytics Platform: Databricks brings together big data and AI in a single platform. Thus, it eliminates the need for disparate tools. This unified approach accelerates innovation by allowing data teams to collaborate more effectively.
Apache Spark Integration: As the brainchild of Apache Spark developers, Databricks offers optimized Spark performance. Users can run large-scale data processing tasks with faster speeds and improved reliability compared to standard Spark deployments.
Interactive Workspaces: Databricks provides collaborative, interactive notebooks. These support multiple programming languages, including Python, Scala, SQL, and R. The notebooks facilitate collaborative data exploration, visualization, and sharing of insights.
MLflow Integration: Databricks has integrated MLflow, an open-source platform for managing the machine learning lifecycle. This allows data scientists to track experiments, package code into reproducible runs, and share and deploy models with ease.
Delta Lake: One of Databricks’ standout features is Delta Lake. This is a storage layer that brings ACID transactions to Apache Spark and big data workloads. It ensures data reliability, improves performance, and simplifies data pipeline architectures.

Microsoft Fabric vs Databricks: Usage Differences

Setting Up Microsoft Fabric

These are the requisite steps to set up Microsoft Fabric:

Prerequisites:

Before you can enable Microsoft Fabric, you must have one of the following admin roles:

Microsoft 365 Global admin
Power Platform admin
Fabric admin

Enabling Fabric for Your Tenant:

Navigate to Tenant Settings: Go to the admin portal and find Microsoft Fabric under tenant settings.
Uncheck Default Selection: There’s a checkbox that says “Accept Microsoft’s default selection (Off for the entire organization).” Uncheck this.
Enable the Switch: Turn on the “Users can create Fabric items (public preview)” switch.
Specify Security Groups (Optional): If you want to enable Microsoft Fabric for specific users, you can specify the security groups.

Enabling Fabric for a Specific Capacity:

Navigate to Capacity Settings: Go to the admin portal and select the capacity you want to enable Microsoft Fabric for.
Override Tenant Admin Selection: Check the “Override tenant admin selection” checkbox.
Enable the Switch: Make sure the “Users can create Fabric items (public preview)” setting is enabled.
Specify Security Groups (Optional): You can specify which security groups should have access.

Setting Up Databricks

This is how a user can set up Databricks.

Prerequisites:

An active Azure or AWS subscription, depending on where you want to deploy Databricks.
Familiarity with the cloud platform’s portal (Azure Portal or AWS Management Console).

Databricks Workspace Creation:

Navigate to your cloud platform’s portal.
Search for “Databricks” and select the Databricks service.
Click “Create” or “Launch” to initiate the setup.
Provide necessary details such as workspace name, subscription, and resource group (for Azure) or specify the deployment options (for AWS).

Configuration:

Choose the pricing tier. Databricks offers both standard and premium options, with premium offering enhanced security and collaboration features.
Select the region closest to your data sources or users for optimal performance.

Virtual Network Setup:

For enhanced security, you can deploy Databricks in your virtual network. This ensures controlled access and network isolation.
Follow the cloud platform’s guidelines to set up and connect the virtual network.

Databricks Cluster Creation:

Once your workspace is ready, navigate to it.
Click on “Clusters” and then “Create Cluster”.
Specify the cluster name, runtime version, and node types. Databricks will manage the cluster’s resources automatically.

Notebook Creation:

In the Databricks workspace, select “Workspace” and then “Create” > “Notebook”.
Choose your preferred language (Python, Scala, SQL, or R) for the notebook.
Use this notebook for data exploration, analysis, and machine learning tasks.

Data Integration:

Databricks integrates seamlessly with various data storage solutions.
Navigate to “Data” in the workspace, and you can easily connect to data sources like Azure Blob Storage, AWS S3, or databases.

Security and Access Control:

Ensure you set up role-based access control to restrict access to sensitive data and operations.
Databricks also supports integration with enterprise identity providers for authentication.

Databricks vs Microsoft Fabric: Cost-Effectiveness

Microsoft Fabric Pricing

Fabric per Capacity plan provides a shared pool of capacity that powers all capabilities in Microsoft Fabric. The benefit is simplified purchasing with a single pool of compute for every workload.

The pricing for this option varies based on the number of CUs, with options ranging from 2 CUs at $0.36 per hour or $262.80 per month, up to 2048 CUs at $368.64 per hour or $269,107.20 per month.

Free trial

Microsoft Fabric was launched as a public preview and was provided free of charge for Power BI users for sixty days.

Databricks Pricing

Source: Databricks

Databricks follows a usage dependent pricing model, where you pay for resources you actually use.

Pricing is determined by factors like the number of virtual machines, runtime hours, and data storage. Databricks offers different pricing tiers to cater to varying requirements:

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

Free trial

Databricks offers a 14-day free trial. However, please note that you will still be charged by your cloud provider for resources like compute instances.

Microsoft Fabric vs Databricks: Security Concerns

Microsoft Fabric Encryption and Authorization

Data Fabric Microsoft ensures that built-in security and reliability features secure your data at rest and transit.
It offers features like conditional access, resiliency, lockbox, and service tags.
Microsoft Fabric also supports managing secrets in a Service Fabric application. Secrets can be any sensitive information, such as storage connection strings, passwords, or other values that should not be handled in plain text.

Databricks Encryption and Authorization

Source: Databricks

Databricks provides encryption features to help protect your data.
It supports adding a customer-managed key to help protect and control access to data.
Databricks also uses a combination of Fernet encryption libraries, user-defined functions (UDFs), and Databricks secrets to encrypt information.

Security Certification

Both Microsoft Fabric and Databricks hold security certifications such as SOC 2 Type 2, ISO 27001, and HIPAA.

Microsoft Fabric

Part of the Office 365 Compliance Framework, covering SOC 1, SOC 2, ISO 27001, HIPAA, and EU Model Clauses.
Undergoes annual SOC 1 Type 2 and SOC 2 Type 2 examinations.
ISO/IEC 27001 certified.

Databricks

They share an annual SOC 2 Type II report.
Participates in independent third-party audits covering SOC 1 Type II, SOC 2 Type II, ISO 27001, ISO 27017, ISO 27018, and HIPAA.

Databricks vs Microsoft Fabric: Availability and Cloud Support

Microsoft Fabric

Fabric is available in various regions across the globe, including but not limited to Asia Pacific, Europe, South America and North America, Middle East and Africa.

Microsoft Fabric offers multi-cloud support, allowing businesses to integrate data from various cloud providers, including Amazon S3 and Google storage.

Databricks

It is available in most regions across the globe, including but not limited to Asia Pacific (Tokyo, Seoul, Mumbai, Singapore, Sydney), Canada (Central), EU (Frankfurt, Ireland, London, Paris), South America (Sao Paulo), and US West (Northern California, Oregon) and US East (Northern Virginia, Ohio).

Databricks can be hosted on Amazon AWS, Microsoft Azure, and Google Cloud Platform.

Microsoft Fabric vs Databricks: Comparative Table

Here’s the updated comparison table combining the information about Databricks vs Microsoft Fabric.

Feature/AspectMicrosoft FabricDatabricksFounded20232013Microsoft Fabric vs Databricks UsageComplex setup process; Uses Azure as a cloud platformEasier setup; Uses Azure, AWS, and GCP as cloud platformsAzure Databricks vs Microsoft Fabric Cloud Platform SupportMulti-cloud supportAmazon AWS, Microsoft Azure, Google Cloud PlatformAzure Databricks vs Microsoft Fabric Security CertificationsSOC 2 Type 2, ISO 27001, HIPAASOC 2 Type II, ISO 27001, HIPAADatabricks vs Microsoft Fabric Pricing OptionsPay-as-you-go hourly or monthlyConsumption-based pricing modelMicrosoft Fabric vs Azure Databricks Free TrialYes, 60 daysYes, 14 days

Which One is Right for You?

The optimal choice between Microsoft Fabric and Databricks depends on the requirements of your business and the nature of your industry.

If you’re seeking an all-encompassing analytics ecosystem that integrates seamlessly with Azure services and offers built-in support for container orchestration and stateful services, Microsoft Fabric is your go-to platform. Its architecture is designed for scalability and load balancing, making it ideal for enterprises that require a unified environment for data engineering, machine learning, and business intelligence.

On the other hand, if your focus is on a platform that excels in big data processing and machine learning with optimized Apache Spark performance, Databricks is the platform for you. It offers a cloud-agnostic approach, available on AWS, Azure, and Google Cloud, and provides specialized features like Delta Lake for data reliability and MLflow for managing the machine learning lifecycle.

The Importance of a Credible Analytics Consultancy Partner

Enterprises looking to use data analytics have to be very specific about their need for the technology. Considering their industry and the type of data they have to analyze, businesses need customized data analytics solutions tailored to their unique requirements with their own set of data.

From selecting the right technologies and integrating them into existing business systems to ensuring data security and regulatory compliance, the challenges are numerous. This is why it is pivotal for companies to choose the right data analytics consulting firm to work with. Here are some benefits of partnering with data analytics consulting firms:

Methodology Rooted in Proven Success Metrics

A reliable data analytics implementation partner brings experience and a time-tested process. A roadmap that has been refined through multiple successful past implementations. This level of expertise not only speeds up the deployment but also mitigates risks. Simultaneously, ensuring that common implementation pitfalls are avoided.

Domain-Specific Expertise and Ethical Compliance

A credible consulting partner provides a thorough command of all the latest data analytics technology and a nuanced understanding of the particular industry in which your organization functions. This is pivotal for customizing data analytics solutions to address your requirements while concurrently adhering to ethical and legal mandates — particularly vital in sensitive sectors such as healthcare or insurance.

Comprehensive Technological Frameworks and Instrumentation

Engaging with a partner endowed with an extensive portfolio of frameworks and tools can be transformative for your enterprise. These resources facilitate every facet of the implementation lifecycle, from data acquisition and analytical processing to ongoing surveillance and maintenance.

Kanerika — Your Data Analytics Implementation Partner

The biggest asset to a business is partnerships with credible agencies that can understand business requirements and customize technologies to achieve results. Enter Kanerika, a distinguished leader with over two decades of proven expertise in data management, AI/ML, generative AI, and data analytics.

Our team of over 100 seasoned professionals is proficient in all the leading data analytics technologies, ensuring you remain at the cutting edge of technological innovation. As a proud Microsoft Gold Partner, our privileged access to Microsoft Fabric’s advanced suite and Azure Databricks amplifies your existing infrastructure, keeping you perpetually ahead of the curve.

With a track record of successful, scalable, and future-proof data analytics projects, Kanerika offers a robust, end-to-end solution that is technologically sound and compliant with emerging regulations.

Choose Kanerika and embark on an accelerated journey to innovation and success.

FAQ

1. What is the difference between Microsoft Fabric and Databricks?

Microsoft Fabric is an integrated analytics platform that offers a unified environment for data engineering, data science, machine learning, and business intelligence. It’s built on Azure technologies and is designed for scalability and load balancing. Databricks, on the other hand, is a cloud-agnostic platform built on Apache Spark, specializing in big data processing and machine learning.

2. Does Microsoft Fabric use Databricks?

No, Microsoft Fabric and Azure Databricks are distinct services, although both can be part of the Azure ecosystem. Microsoft Fabric integrates various Azure services, but it is not built on or does not inherently use Databricks.

3. Why use Databricks instead of AWS?

While AWS offers its own set of big data and analytics services, Databricks provides a unified analytics platform with optimized Apache Spark performance. Databricks allows for faster data processing and has specialized features like Delta Lake and MLflow, which may not be readily available in AWS’s native services.

4. Why use Databricks instead of Azure?

Databricks offers a cloud-agnostic approach and is optimized for Apache Spark, which can result in faster data processing tasks. Azure offers a broad set of services, but if your primary focus is big data and machine learning with Spark, Databricks could be more aligned with your needs.

5. What is the difference between Microsoft Fabric and Azure?

Microsoft Fabric is a specific service within the Azure ecosystem designed for unified data analytics. Azure is the broader cloud platform that hosts a variety of services, including but not limited to analytics, computing, storage, and databases.

6. What is the equivalent of Databricks in Azure?

The closest equivalent to Databricks in Azure would be Azure Databricks, which is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform.

7. Does Microsoft Fabric replace Snowflake?

No, Microsoft Fabric and Snowflake serve different purposes. While both are data platforms, Microsoft Fabric offers a unified analytics environment, whereas Snowflake is a cloud-based data warehousing service. They can complement each other but are not direct replacements.

8. Why is Databricks better than Snowflake?

“Databricks better than Snowflake” is subjective and depends on your specific needs. Databricks excels in big data processing and machine learning, offering optimized Spark performance. Snowflake is designed for cloud-based data warehousing and excels in data storage and SQL-based data manipulation. Each has its own set of advantages depending on the use-case.

This blog was originally published at https://kanerika.com on October 30, 2023.