Most businesses these days are struggling to modernize their data architecture while keeping business operations running smoothly. Research from Gartner indicates that 75% of organizations will shift from piloting to operationalizing artificial intelligence by 2024, making platform transitions like Databricks to Fabric migration increasingly critical for enterprise success.
As enterprises scale their data operations, the shift from Databricks to Microsoft Fabric represents more than just a platform change—it’s a transformative journey that can reshape how organizations handle their data ecosystem. This comprehensive Databricks to Fabric migration roadmap cuts through the complexity, offering enterprise leaders a clear path forward.
Meet Kanerika at #FabCon 2025 to Elevate Your Data and AI Strategy – Register Now Databricks vs Microsoft Fabric: Key Differences Databricks and Microsoft Fabric are both data platforms that can be used to manage data and analytics, but they are designed for different audiences and have different capabilities:
Microsoft Fabric A unified platform that combines data and analytics tools, including data engineering, data science , machine learning, and business intelligence. It’s designed to be user-friendly and easy to integrate and is ideal for business users. Fabric uses Azure’s Lakehouse solution, OneLake, to store data.
Databricks A cloud-based platform that specializes in big data analytics , artificial intelligence, and distributed computing. It’s designed for more technical data professionals and requires coding expertise. Databricks can work on Azure , AWS, or GCP.
Here are the key differences between these two analytics platforms:
1. Data Flow and Pipeline Management Microsoft Fabric introduces the Lakehouse structure, which simplifies data flow and pipeline management by consolidating data storage, transformation, and querying within a single ecosystem. This Lakehouse model enables more straightforward and faster data processing workflows compared to Databricks’ data lake approach, where separate data flows and additional integrations are often necessary to manage various types of data. With Fabric’s structured Lakehouse, businesses benefit from a more organized data architecture, reducing complexity and improving pipeline efficiency.
2. Architecture Fabric combines compute, data transfer, storage volume, and other costs into a single SKU called “Capacity Units”. Databricks allows customers to configure their own infrastructure and is charged DBUs for managing job execution.
3. Data Querying Fabric offers significant advantages in data querying with its direct table shortcuts and semantic models, which allow users to create optimized queries for reporting and analytics. In contrast, Databricks typically requires more manual setup to handle complex querying across large datasets. By leveraging Fabric’s semantic models, organizations can establish direct links to data tables, enabling faster access and more streamlined data querying processes , especially for real-time analytics.
4. Resource Consumption and Costs Microsoft Fabric provides more flexibility in managing resources, particularly by allowing users to choose between shortcuts and direct data writing. Shortcuts enable data to remain stored within Databricks while being accessed directly in Fabric, minimizing the need for duplicate data flows and cutting down on storage and transfer costs. Additionally, with the option to centralize operations within Fabric, businesses can consolidate resource usage, preventing the need to run both platforms simultaneously, thus saving on computational expenses.
5. Integration Flexibility Fabric excels in cross-platform interoperability with native integration capabilities for Power BI, Azure Synapse, and the broader Microsoft ecosystem. This seamless compatibility provides an advantage for organizations already using Microsoft tools, as it enables effortless data sharing and interaction across platforms. In contrast, Databricks often requires third-party tools or additional configurations to achieve the same level of cross-platform integration. Fabric’s unified environment allows teams to work more collaboratively and efficiently, leveraging Microsoft’s ecosystem for a more cohesive data strategy .
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Aspect Databricks Microsoft Fabric Platform Architecture Unified analytics platform built on Apache Spark All-in-one analytics platform integrating multiple Microsoft services Data Lake Integration Delta Lake (proprietary optimized storage layer) OneLake (unified storage with delta lake compatibility) Cost Structure DBU-based pricing with separate compute and storage costs Capacity-based pricing with unified cost model Native Integration Requires connectors for Microsoft tools Native integration with Microsoft ecosystem (Power BI, Azure, Office 365) Development Environment Notebooks with support for multiple languages (Python, R, SQL, Scala) Notebooks plus Microsoft-specific tools like Data Factory , Synapse Machine Learning Capabilities MLflow integration with extensive ML features Azure Machine Learning integration with AutoML capabilities Real-time Analytics Structured Streaming with Delta Live Tables Real-time analytics through Synapse Real-Time Analytics Governance & Security Unity Catalog for data governance Purview integration for unified data governance Deployment Options Multi-cloud (AWS, Azure, GCP) Primarily Azure-focused Collaboration Features Basic collaboration through workspace sharing Deep integration with Microsoft Teams and Office 365 Data Warehousing Requires separate configuration Built-in data warehouse capabilities ETL/ELT Processing Delta Live Tables and standard Spark ETL Data Factory with mapping data flows Query Performance Photon engine for SQL acceleration Microsoft’s latest query engine built on Lake Database BI Integration Third-party BI tool integration required Native Power BI integration Open-source Support Strong open-source foundation (Apache Spark) Mix of proprietary and open-source technologies
Databricks to Microsoft Fabric: Pre-Migration Assessment & Planning 1. Current State Analysis Data Inventory and Classification: A comprehensive audit of all data assets across your Databricks environment, including data types, volumes, and sensitivity levels. This involves cataloging databases, tables, files, and their relationships while identifying critical vs. non-critical data . Understanding your data landscape helps prioritize migration sequences and establish appropriate security measures in Fabric.
Workload Assessment: Detailed analysis of existing jobs, notebooks, pipelines, and their execution patterns in Databricks. This includes identifying peak usage times, resource consumption patterns, and interdependencies between different workloads. Understanding these patterns ensures proper capacity planning and resource allocation in Microsoft Fabric.
Dependencies Mapping: Documentation of all internal and external system dependencies, including data sources , APIs, scheduling tools, and downstream applications. This mapping helps identify potential bottlenecks, integration requirements, and necessary modifications for compatibility with Fabric’s architecture.
Resource Utilization: Metrics Collection and analysis of current compute, storage, and memory usage patterns in Databricks. This involves gathering metrics on cluster utilization, job durations, and storage consumption patterns to right-size your Fabric environment and optimize costs.
2. Business Impact Evaluation Cost-Benefit Analysis: Detailed comparison of current Databricks operational costs against projected Fabric expenses, including licensing, storage, compute, and maintenance costs. This analysis should factor in both immediate migration costs and long-term operational savings to justify the transition.
ROI Projections: Calculation of expected return on investment, considering factors like improved performance, reduced maintenance, enhanced integration capabilities, and operational efficiencies. This helps secure stakeholder buy-in and establish realistic expectations for the migration benefits.
Risk Assessment: Identification and evaluation of potential risks, including data loss, service interruptions, performance degradation, and compliance issues. This assessment helps develop mitigation strategies and contingency plans to ensure business continuity during migration.
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3. Stakeholder Alignment Migration Team Structure: Definition of the core migration team, including technical leads, business analysts, data engineers, and subject matter experts. This involves clarifying roles, responsibilities, and decision-making authority to ensure smooth execution of the migration plan.
Communication Strategy: Development of a clear communication plan to keep all stakeholders informed about migration progress, challenges, and milestones. This includes establishing regular update channels, feedback mechanisms, and escalation procedures for issue resolution.
Training Needs Analysis: Assessment of current team skills against required capabilities for Microsoft Fabric operations. This helps identify training gaps and develop appropriate learning paths to ensure team readiness for the new platform.
4. Project Management Framework Timeline Developmen t: Creation of a realistic migration timeline with clear phases, milestones, and dependencies. This involves considering business cycles, resource availability, and critical business events to minimize disruption during the transition.
Success Metrics: Definition Establishment of clear, measurable criteria for migration success, including technical performance benchmarks, user adoption rates, and business impact metrics. This provides objective measures to track progress and validate migration outcomes.
Budget Planning: Detailed budgeting for all aspects of the migration, including software licenses, infrastructure costs, consulting services , and training expenses. This ensures adequate resource allocation and helps prevent cost overruns during the migration process .
Change Management : Plan Development of strategies to manage the organizational impact of the platform transition. This includes user adoption plans, resistance management, and processes to ensure smooth operational handover to the new platform.
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Advantages of Migrating to Microsoft Fabric – The Kanerika Solution As a trusted Microsoft data and AI solutions partner, Kanerika brings expertise to every step of your Fabric migration, ensuring seamless integration and optimized data operations . With Kanerika’s approach, expect enhanced data performance, streamlined costs, and scalable, future-ready analytics that drive real business outcomes .
1. Improved Data Integration and Management Microsoft Fabric stands out by providing an integrated Lakehouse architecture, which simplifies data storage, access, and integration. Unlike Databricks, which requires setting up multiple data flows and managing complex integrations , Fabric’s Lakehouse enables a more unified approach.
Through the use of Dataflow Gen 2, Fabric allows businesses to create a data pipeline that unifies data from diverse sources into a single, manageable Lakehouse environment. This not only enhances data integrity but also improves accessibility for teams needing centralized reporting and analytics, effectively streamlining data management across departments.
One of the standout features of Microsoft Fabric is its use of semantic models and direct table shortcuts for optimized data querying and reporting. This architecture significantly reduces query times, especially for complex datasets, by allowing Fabric to perform more efficient data queries directly on its semantic models.
For instance, Kanerika’s Fabric implementation demonstrated the benefit of transitioning existing reports (like the ContainerUtilization_POC) into the semantic model, which enhanced query response and reporting speed. Additionally, Fabric’s scalability ensures that as data needs grow, enterprises can adapt quickly without costly system overhauls.
3. Cost-Efficiency Fabric’s architecture offers various options to minimize operational costs. By utilizing data shortcuts and direct publishing capabilities, Fabric users can maintain data storage efficiency while reducing the need for additional data flows. For instance, shortcuts allow data to remain in Databricks while being accessed seamlessly through Fabric without incurring high transfer or storage fees.
Moreover, Fabric’s ability to consolidate processes into a single ecosystem cuts down on duplicated resource usage, allowing enterprises to reduce reliance on multiple platforms and lower associated operational expenses.
4. Native Integration with Microsoft Ecosystem For organizations already invested in Microsoft’s suite of tools, Fabric’s native integration with Power BI, Microsoft 365, and Azure is invaluable. This direct integration enables smoother data flow and sharing across the ecosystem, allowing data from Fabric’s Lakehouse to be readily visualized in Power BI or processed within Azure Synapse for further analytics.
This compatibility eliminates the need for third-party integrations and enhances usability, as team members can leverage familiar tools to manage, visualize, and analyze data without additional training or system adjustments.
5. Optimized Resource Usage Fabric’s unified environment offers businesses the ability to consolidate data processes , reducing resource consumption and operational overhead. By shifting data operations and execution directly onto Fabric, teams can avoid the need to operate both Fabric and Databricks simultaneously. This approach not only conserves computational resources but also reduces costs associated with running parallel engines.
Additionally, Kanerika’s recommendation to transition code execution to Fabric over time further emphasizes its long-term resource efficiency, as it minimizes the heavy reliance on both platforms for ongoing report queries and data management .
Amit Chandak, Chief Analytics Officer at Kanerika says
“Transitioning from Databricks to Microsoft Fabric addresses the critical need for a unified, efficient data architecture, especially as data complexities intensify. Microsoft Fabric’s Lakehouse model and semantic layers streamline data querying and reporting, directly boosting scalability and performance. For our clients, this migration has enhanced real-time data accessibility and optimized resource usage, achieving seamless integration within their existing Microsoft ecosystems while significantly lowering operational costs.”
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Best Practices for Migrating from Databricks to Microsoft Fabric: 1. Data Structuring and Semantic Model Setup For effective migration, it’s essential to segment Lakehouses by specific functions (e.g., dimensions or metrics) and create custom semantic models to enable flexibility. This approach allows teams to:
Keep data organized and easy to manage across different business needs. Enable advanced features like calculation groups and field parameters, improving usability. Prevent performance slowdowns by minimizing the number of complex joins. 2. Utilize Shortcuts for Data Access Implementing shortcuts in Fabric allows for direct data access without duplicating flows, reducing workload and enhancing efficiency. Key benefits of using shortcuts include:
Simplifying the data architecture by minimizing redundant data flows. Allowing Fabric to read data directly from Databricks, reducing storage and transfer costs. Speeding up data access and ensuring that reports reflect real-time information. Regular testing of resource consumption across Databricks and Fabric ensures an optimized and cost-effective migration . To effectively monitor performance:
Analyze resource usage during report interactions to fine-tune configurations. Identify bottlenecks in data flows and apply adjustments for smoother operations. Ensure efficient utilization by consolidating processes where possible. 4. Security Implementation Securing data is paramount, and Row-Level Security (RLS) within Power BI allows for controlled, user-specific access, maintaining data confidentiality and integrity. Best practices for RLS include:
Setting user roles and permissions to restrict data access based on roles. Applying RLS configurations in Power BI for seamless integration with Fabric. Using the ALM Toolkit simplifies the migration of complex relationships and measures between models, helping avoid manual errors and improving consistency. Key uses of the ALM Toolkit are:
Migrating relationships, calculations, and hierarchies efficiently between Fabric models. Ensuring continuity in data models without losing established relationships. Saving time and reducing complexity, especially when dealing with large datasets. Microsoft Fabric Vs Tableau: Choosing the Best Data Analytics Tool Compare Microsoft Fabric and Tableau to find the right data analytics tool for your business needs, efficiency, and insights.
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Case Study: Implementation of Kanerika’s Migration Solution A prominent logistics company faced challenges in managing large volumes of data across global operations. Their existing data framework, based on Databricks, was becoming costly and complex to scale with rising demands. By transitioning to Microsoft Fabric with Kanerika’s expertise, they successfully optimized their data workflows and enhanced reporting capabilities. Through Fabric’s Lakehouse architecture, the company gained:
Cost Efficiency: Reduced operational costs by consolidating resource usage within the Microsoft ecosystem. Improved Reporting and Analytics: Leveraged Fabric’s semantic models to streamline and enhance data querying for real-time insights. Enhanced Data Security: Integrated RLS in Power BI, securing sensitive data and ensuring role-based access control. Kanerika’s Fabric Expertise: Efficient Migration and Implementation Services As a recognized Microsoft Data and AI solutions partner , Kanerika stands at the forefront of Microsoft Fabric implementation, being among the first firms to master and deploy this revolutionary platform. Our deep expertise in Fabric enables organizations to seamlessly transition their data ecosystems while maximizing ROI and minimizing operational disruptions.
At Kanerika, we architect custom migration solutions that modernize data platforms , transforming legacy systems into cost-effective, future-ready environments. Our automated migration frameworks have consistently delivered successful transitions across diverse technology stacks, including Databricks to Fabric, SSIS to Fabric, Tableau to Power BI, SSRS to Power BI, Informatica to Talend, Informatica to DBT, and UiPath to Power Automate .
What sets us apart is our commitment to designing tailored solutions that align perfectly with each client’s unique business objectives. Our proven methodology combines technical excellence with strategic insight, ensuring high-impact results across industries. By leveraging our extensive experience and automated tools, we accelerate migration timelines while maintaining data integrity and business continuity .
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Frequently Asked Questions How to integrate Databricks with Microsoft Fabric? Connecting Databricks to Microsoft Fabric streamlines your data workflow by allowing Databricks to serve as a powerful compute engine for Fabric's analytical capabilities. Essentially, you leverage Databricks' processing power for data transformation and analysis, then visualize and share the results within the Fabric workspace. This integration typically involves configuring linked services and datasets within Fabric, pointing them to your Databricks resources. The specific steps depend on your Fabric and Databricks setup but generally involve minimal coding.
What is the difference between Microsoft Fabric and Databricks? Microsoft Fabric is a unified analytics platform built entirely within the Azure ecosystem, offering a streamlined, integrated experience for data engineering, analytics, and visualization. Databricks, while also powerful, is a more independent platform offering similar capabilities but with greater flexibility and broader cloud support (beyond Azure). The key difference lies in integration: Fabric prioritizes deep Azure synergy, while Databricks prioritizes flexibility and openness. Ultimately, the "best" choice depends on your existing cloud infrastructure and preference for tightly-coupled vs. loosely-coupled architectures.
How to connect Databricks to HDFS? Connecting Databricks to HDFS involves configuring your Databricks cluster to access your HDFS storage. This usually means specifying the HDFS namenode address and potentially configuring authentication mechanisms like Kerberos. You'll then use standard Spark commands within Databricks notebooks to read and write data directly to your HDFS paths. Successful connection requires proper network connectivity and configured access credentials.
Why migrate to Microsoft Fabric? Microsoft Fabric consolidates your data warehousing, analytics, and data engineering needs into a single, unified platform. This simplifies your data stack, reducing complexity and cost associated with managing disparate tools. It offers enhanced collaboration and a modern, intuitive user experience, streamlining your entire data lifecycle. Ultimately, it empowers faster, more insightful data-driven decision-making.
How do I connect Google sheets to Databricks? Connecting Google Sheets to Databricks leverages Databricks' ability to read data from various sources. You don't directly connect; instead, you use Databricks' built-in connectors (like the JDBC/ODBC driver for Google Sheets, if available, or via a connector library) to import your Google Sheet data into a Databricks table or DataFrame. This imported data can then be processed and analyzed within Databricks. Consider alternatives like exporting the sheet to CSV for simpler import if direct connection proves difficult.
How do I import a library into Databricks? Importing libraries in Databricks is straightforward. You use the familiar `import` statement within your notebook cells, just like in a standard Python environment. Databricks automatically handles most common library installations; if needed, you can install using `%pip install `. This installs the library within your current Databricks cluster.
How do I connect Databricks to Airflow? Connecting Airflow to Databricks lets you orchestrate Databricks jobs within your Airflow workflows. This is typically achieved using the `DatabricksOperator`, a custom Airflow operator, which leverages your Databricks access token for authentication and submits jobs to your workspace. Essentially, Airflow acts as a conductor, triggering and monitoring Databricks tasks as part of a larger data pipeline. This integration offers powerful workflow management capabilities.
