Article
Choosing the right platform: Microsoft Fabric vs Databricks
Oct 11, 2024 · Authored by Chris Wagner
The ability to successfully harness your organizational data is a critical function for organizations looking to compete in today’s data-driven world. Utilizing robust data platforms can fundamentally change the way your organization harnesses data to drive insights and make informed data-driven decisions. Microsoft Fabric and Microsoft Databricks are two powerful data platforms offering comprehensive solutions for data management, analytics and business intelligence – each with unique strengths tailored to different organizational needs.
Microsoft Fabric, a SaaS cloud-based unified analytics platform, seamlessly integrates with the broader Microsoft ecosystem, making it an ideal choice for businesses heavily invested in tools like Power BI and Excel. On the other hand, Databricks, built on Apache Spark, excels in building, deploying and supporting big data and AI-driven analytics and is favored by organizations seeking robust machine learning (ML) and data science capabilities.
Choosing between these platforms depends on factors such as your organization's strategic goals, existing infrastructure, analytics needs and the importance of artificial intelligence (AI) integration – so which platform is right for you?
Data engineering: Building scalable pipelines
Data engineering is a cornerstone of any data-driven platform, and both Fabric and Databricks excel in providing strong infrastructure for building data pipelines and processing large datasets.
Fabric offers a low-code, highly integrated platform that simplifies data pipeline creation and management for organizations already using the Microsoft ecosystem.
Simplifying data engineering tasks with its emphasis on low-code/no-code solutions and ETL/ELT tools ensures that a wide range of users with varying technical skills can utilize the platform to streamline data and analytics workflows. Offering fully integrated, pre-built solutions with its Data Factory capabilities, Fabric simplifies the ability to design, implement and manage end-to-end data pipelines across multiple data sources. Fabric provides a fully managed Apache Spark compute platform, with features including rapid session initialization and the dynamic scaling of Spark clusters which reduce time spent managing infrastructure so engineers can focus on deriving insights.
Databricks provides robust, scalable Spark-based data engineering capabilities for processing large datasets in real-time, ideal for complex data workflows.
Providing engineers with more granular control over their data pipelines, Databricks’ optimized Spark performance efficiently handles large-scale data processing, supports organizations managing complex data ecosystems. Its open architecture supports various data engineering frameworks and languages and easy integration with various cloud providers and analytics tools, ensuring flexibility and control during large-scale data engineering projects. By fostering teamwork among engineers, Databricks’ enables an efficient collaborative workspace for complex data tasks.
Data analytics: Driving business insights
When it comes to analytics, both Fabric and Databricks provide robust solutions, though they cater to different user profiles and needs.
Fabric delivers user-friendly analytics through its seamless integration with Power BI, making it accessible to business users across the organization.
Integrated with Power BI, Fabric simplifies reports and dashboards creation through self-service analytics, making insights more accessible throughout the organization. Its integration with other Microsoft products such as Excel allows for seamless data analysis, enabling non-technical users to derive value from their data. With its emphasis on ease of use, Fabric is ideal for organizations looking to easily gain insights and leverage data analytics to make data-driven decisions.
Databricks offers flexibility for large-scale advanced analytics, including real-time data and ML driven insights, ideal for high-performance environments.
When dealing with unstructured data or massive data lakes, Databricks’ support for various analytical frameworks ensures an organization can confidently handle advanced analytics for large-scale and complex data projects. Its support for ML driven analytics enables data scientists to apply more advanced models to their data while its notebook-based environment allows teams to collaboratively explore data, share insights and build highly developed analytical models – ideal for organizations with sophisticated analytics requirements.
Data science: AI and ML capabilities
Data science is a strength for Databricks, due to its deep roots in Apache Spark and scalable ML library – but Fabric’s integrations still offer robust solutions for organizations looking to build, deploy and manage advanced data science and ML models.
Fabric integrates well with Azure Machine learning to support data science abilities.
Integrating well with Azure Machine Learning, Fabric provides users with access to various ML capabilities within a more familiar environment while allowing data scientists to easily transition from data preparation to model development and deployment on a single platform. For organizations already using Microsoft’s ecosystem, this integration may prove beneficial as it supports accessibility to users across the organization with different levels of expertise.
Databricks offers a collaborative environment with extensive ML libraries, ideal for advanced data science and AI projects.
Offering a real-time, highly collaborative environment and supporting languages such as Python, R, Scala and SQL, Databricks enables effective sharing of insights, code and models. Its robust ML and AI capabilities enable data scientists to build, train and deploy models at scale – enhancing the productivity and efficiency of your data science team. Databricks also supports experimentation with various models, allowing for rapid iteration and tuning.
DevOps: Streamlining data operations
DevOps capabilities are critical for ensuring smooth deployment and management of data applications. Both Fabric and Databricks provide robust solutions that empower DevOps teams to refocus their time on automation and collaboration.
Fabric integrates seamlessly with Azure DevOps and GitHub, offering familiar CI/CD workflows ideal for streamlined DevOps processes.
Providing excellent DevOps capabilities through its integration with Azure DevOps and GitHub, Fabric empowers developers to add their development tools, processes and best practices into the platform. Users can manage their data workflows and application deployments using tools they are familiar with, which reduces friction in the development and deployment process and makes it easier for teams to manage their data workflows. Fabric’s strength lies in its tight coupling with Azure, which enhances security, monitoring and version control throughout the application lifecycle.
Databricks provides detailed control over cluster management and scalable CI/CD pipelines, ideal for managing complex Spark-based environments.
Offering comprehensive DevOps features and support for Git integration for version control and CI/CD workflows, Databricks helps data professionals ensure their code changes are effectively tested and deployed to maintain the integrity of their data applications. Its environment management is robust, providing fine-grained control over cluster management, scaling and resource allocation. With a focus on managing Spark-based environments, Databricks is ideal for organizations seeking highly scalable environments that need detailed control over Spark workloads.
Low-code vs pro-code: Meeting users at their skill level
The flexibility to choose between low-code and pro-code approaches is a key factor for many organizations, with Fabric and Databricks catering to different organizational needs by offering different levels of coding flexibility.
Fabric focuses on low-code development, empowering business users to create workflows and applications with minimal coding experience.
Focused on low-code/no-code solutions with its integration of Microsoft Power Apps and Power Automate, Fabric empowers business users with varying technical skills to create workflows, automate processes, and build applications without deep coding expertise. Users can easily use develop and deploy data solutions with the drag-and-drop interfaces and prebuilt templates. This democratization of data and application development makes Fabric ideal for companies looking to enable more users across the business to interact with data.
Databricks caters to pro-code users by offering flexibility and control for developers and data engineers to build custom, code-heavy data solutions.
Designed for pro-code users with coding experience, Databricks provides some visual interfaces, but most of its power is unlocked by skilled developers and data engineers who can write Spark jobs, manage clusters and build models. Supporting various programming languages and frameworks allows users to perform complex transformations, construct custom data pipelines and develop ML models. Organizations with advanced technical teams and data requirements will appreciate the control, customization and flexibility to tackle complex data challenges that Databricks offers.
Security: Protecting data assets
Security is a top priority for both Microsoft Fabric and Databricks, though each platform offers different levels of control and compliance.
Fabric benefits from Azure’s built-in security and compliance features, providing robust protection and compliance for organizations across industries.
With Azure’s built-in security and compliance features, Fabric offers identity management via Azure Active Directory, authentication with Microsoft Entra ID, encryption at rest and role-based access controls – ensuring a secure environment for data operations with interactions across the platform authenticated and encrypted. Including features like data loss prevention, data lineage and information protection labels and compliant with a wide range of industry standards (GDPR, HIPAA, ISO, etc.) helps organizations maintain control of their data and comply with various regulatory standards.
Databricks delivers robust access control and security features, making it a reliable choice for handling sensitive, large-scale data workloads.
Providing robust security features, including data encryption, role-based access control and integration with Active Directory, Databricks is known for its fine-grained access controls and supports compliance with a wide range of regulations. Offering authentication methods, like single sign-on, to prevent unauthorized access and comprehensive data governance tools, such as network controls and encryptions, helps organizations manage their data in a secure environment. Databricks ability to provide a secure and compliant environment makes it a strong option for organizations with sensitive data workloads.
Integration with other tools: Extending platform capabilities
Both Fabric and Databricks offer strong integration with other tools, but their ecosystems differ.
Fabric seamlessly integrates with the Microsoft ecosystem, making it an ideal choice for organizations already using tools like Power BI, Excel and Azure.
As part of the Microsoft ecosystem, Fabric integrates seamlessly with other Microsoft products creating a cohesive ecosystem for data and analytics. The platform’s wide array of connectors allows it to integrate with many third-party tools and services while allowing users to connect to 135 different data sources – facilitating efficient ingestion, transformation and analysis of data from various sources. This seamless integration with other Microsoft tools ensures a smooth and efficient workflow, making Fabric ideal for organizations already using Microsoft’s productivity suite.
Databricks integrates with open-source and big data tools like Apache Spark and Delta Lake, offering flexibility for organizations leveraging multiple platforms.
Providing strong integration with big data tools like Delta Lake, Apache Spark and MLflow, Databricks allows organizations to connect their preferred data storage, processing and visualization tools, building a unified data environment. Its collaboration with cloud providers like AWS, Azure and Google Cloud ensures flexibility in deployment, and its integration with popular data storage platforms and third-party analytics tools makes it highly adaptable – an excellent choice for organizations using a range of open-source tools.
Cost management systems: Optimizing budgets
Considering cost management is essential when choosing a data platform, both Fabric and Databricks offer flexible pricing models that cater to varying needs.
Fabric utilizes a pay-as-you-go model to ensure cost-effective resource management.
With a pay-as-you-go cost structure based on consumption, Fabric allows organizations to scale usage based on needs, making it easy to manage costs as usage scales. The built-in bursting and smoothing capabilities allow users to temporarily use more resources than allocated and manage capacity by borrowing unused capacity from quieter periods respectively, speeding up execution times during peak demand ensuring consistent performance. These features simplify capacity management and help organizations monitor and control their spending, ensuring budget predictability while optimizing performance.
Databricks offers scalable, usage-dependent pricing models that provide control over expenses.
Offering a consumption-based pricing model with detailed reporting and cost breakdowns through Databricks Cost Management tools, Databricks allows organizations to optimize costs by controlling compute resources and fine-tuning workloads. Flexibility when it comes to handling massive data processing workloads and scaling resources as needed makes Databricks an appropriate choice for organizations with dynamic data requirements. This helps to manage costs efficiently while maintaining high performance and scalability.
Data governance: Ensuring data compliance
Fabric and Databricks both provide strong data governance solutions designed to manage and safeguard data assets, enabling organizations to maintain full oversight and control of their data ecosystems.
Fabric strengthens governance through Microsoft Purview, offering solutions for data lineage, cataloging and sensitivity labeling to maintain data integrity and regulatory compliance.
Supporting robust data governance through Azure Purview allows organizations to catalog, classify, and manage their data assets efficiently across multi-cloud environments while governing their entire data ecosystem to ensure data is secure, compliant and easily discoverable. Providing data lineage, cataloging and sensitivity labeling helps organizations maintain data integrity and promotes transparency across data pipelines, making it easier for teams to monitor and enforce governance policies.
Databricks leverages Unity Catalog for unified access control, auditing and data discovery, establishing a secure and compliant governance structure across data and AI assets.
Providing strong data governance capabilities through its Unity Catalog, Databricks enables organizations to implement fine-grained access controls, audit logs and data discovery across their data and AI assets. Unity Catalog's integration with various data sources and cloud platforms helps maintain a consistent and secure governance framework across the organization. This makes it particularly appealing for organizations that require advanced governance features to meet internal policy or regulatory compliance requirements.
Choosing the right platform for your organization
Both Microsoft Fabric and Databricks are powerful data platforms, each catering to different aspects of data management, analytics and governance. Microsoft Fabric is an excellent choice for organizations seeking an intuitive, low-code platform that integrates seamlessly with the Microsoft ecosystem, making it ideal for businesses looking for self-service analytics and ease of use. Databricks, on the other hand, is ideal for organizations that require advanced data engineering, ML and deep technical control over their data pipelines.
Ultimately, the right platform depends on your organization's existing infrastructure, specific data requirements and long-term business goals. Whether you prioritize ease of use and integration (Fabric) or scalability and performance for complex data tasks (Databricks), Baker Tilly’s digital team, in collaboration with Microsoft, can help your organization build a strategy for how you can successfully leverage these technologies to propel your organization toward data-driven success.
Interested in learning more? Contact one of our professionals today.
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