When the creators of Apache Spark formed Databricks in 2013, they utiliized 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.
Table of Contents
- The Contenders: Microsoft Fabric and Databricks
- What is Microsoft Fabric?
- What is Databricks?
- Microsoft Fabric vs Databricks: Architecture
- Microsoft Fabric Pricing
- Databricks Pricing
- Microsoft Fabric vs Databricks: Security Features
- Microsoft Fabric vs Databricks: Availability and Cloud Support
- Microsoft Fabric vs Databricks: Comparative Table
- Which One is Right for You?
- The Importance of a Credible Analytics Consultancy Partner
- Kanerika – Your Data Analytics Implementation Partner
- FAQs
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.
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.
Read more: Data Analytics – Exploring the Scope and Opportunities in 2023
Microsoft Fabric vs Databricks: Architecture and Components
Microsoft Fabric’s Unified Data Platform
Microsoft Fabric stands on the foundation of a thoughtfully crafted architecture, integrating a spectrum of essential components to address varied data requirements – a fully managed data management platform that aims to unite multiple roles within an organization. It consists of the following capabilities:
Source: Microsoft
Data Lake: Serving as the cornerstone, the Data Lake is a robust and expandable storage facility. It’s proficient in housing diverse data types, whether structured or unstructured, guaranteeing data consistency and easy access.
Data Engineering: This component is the backbone of the architecture, focusing on the transformation and optimization of data. It ensures data is not just purified but also ready for in-depth analysis.
Data Integration: Bridging the gap, the Data Integration platforms seamlessly merge various data sources. They guarantee smooth data movement and synchronization across different platforms, simplifying data amalgamation.
Machine Learning: For enthusiasts eager to tap into AI’s potential, the Machine Learning platform is invaluable. It’s designed to streamline the development, fine-tuning, and rollout of sophisticated machine learning algorithms, propelling automation and foresight.
Business Intelligence: The Business Intelligence tool specializes in morphing raw data into actionable intelligence. It boasts advanced visualization tools, enabling users to probe data deeply and garner crucial insights, facilitating data-driven decisions.
Read more: From One Lake to Power BI: How Microsoft Fabric Powers Agile Decision-Making For Business Users
Databricks’ Lakehouse Data Framework
Source: Microsoft
Databricks champions the Lakehouse design, an elegant fusion of data lakes’ and data warehouses’ primary features. This design is anchored around key components:
Data Sharing: Databricks advocates for transparent data sharing, fostering smooth collaboration across different platforms. This ensures datasets, models, dashboards, and notebooks are shareable while maintaining rigorous security and governance protocols.
Data Management and Engineering: The platform optimizes data intake and handling procedures. Leveraging automated ETL and the agility of Delta Lake, Databricks metamorphoses your data lake into a central hub for all data forms.
Data Warehousing: Databricks guarantees users access to the most up-to-date and holistic data. Harnessing Databricks SQL, it delivers unmatched price-to-performance metrics compared to conventional cloud data warehouses, facilitating swift insight generation.
Data Science and Machine Learning: The Lakehouse is the cornerstone for Databricks Machine Learning. It’s an all-encompassing solution addressing the entire machine learning spectrum. From data preparation to deployment, the platform, enriched with top-tier data pipelines, expedites machine learning endeavors and enhances team efficiency.
Data Governance: Databricks emphasizes data governance. It presents a consolidated perspective of your data ecosystem, ensuring all-round compliance. With centralized auditing coupled with automated lineage and monitoring tools, data usage tracking is effortless.
Read More – Microsoft Copilot vs ChatGPT: Choosing the Right AI Titan
Microsoft Fabric vs Databricks: Use Cases
Microsoft Fabric Use Cases and Key Features
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.
- 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 bedeveloped, 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 canmaintaine 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 Use Cases and Key Features
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.
Databricks vs Microsoft Fabric: Pricing Model
Microsoft Fabric Pricing
Microsoft Fabric SKU Pricing Plan
Microsoft 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.
Read more: Understanding Microsoft Fabric Pricing And Licensing For Your Business
Databricks Pricing
Pricing Chart for Databricks Pay-As-You-Go Plans
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 Features
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 Certifications and Audits
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.
Read more: Top 10 Opportunities And Challenges Of Data Analytics In Healthcare
Microsoft Fabric vs Databricks: Comparative Table
Here’s the updated comparison table combining the information about Databricks vs Microsoft Fabric.
| Feature/Aspect |
Microsoft Fabric |
Databricks |
| Founded |
2023 |
2013 |
| Microsoft Fabric vs Databricks Usage |
Complex setup process; Uses Azure as a cloud platform |
Easier setup; Uses Azure, AWS, and GCP as cloud platforms |
| Azure Databricks vs Microsoft Fabric Cloud Platform Support |
Multi-cloud support |
Amazon AWS, Microsoft Azure, Google Cloud Platform |
| Azure Databricks vs Microsoft Fabric Security Certifications |
SOC 2 Type 2, ISO 27001, HIPAA |
SOC 2 Type II, ISO 27001, HIPAA |
| Databricks vs Microsoft Fabric Pricing Options |
Pay-as-you-go hourly or monthly |
Consumption-based pricing model |
| Microsoft Fabric vs Azure Databricks Free Trial |
Yes, 60 days |
Yes, 14 days |
Microsoft Fabric and Databricks – Which One is Right for You?
The 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:
Read More: How Kanerika’s Digital Consulting Services can Transform your Business
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.
FAQs
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.
