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.
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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.
Microsoft Fabric: A Game-Changer for Data Engineering and Analytics
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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 Development: 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.
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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
Can I use Databricks in fabric?
Databricks is a cloud-based platform that utilizes its own infrastructure. It is not designed to be deployed or run on physical hardware like a "fabric" environment. Therefore, directly using Databricks within a traditional fabric setup is not possible. You would need to leverage Databricks' cloud services to achieve similar functionality.
What is the difference between Databricks and data fabric?
Databricks is a cloud-based data platform offering a unified workspace for data engineering, data science, and machine learning. It provides tools and services for data storage, processing, and analysis. A data fabric, on the other hand, is an architectural approach that seamlessly connects and integrates various data sources across an organization, enabling unified data governance and access. While Databricks focuses on data processing and analysis, a data fabric emphasizes data accessibility and management across different systems.
Why migrate to fabric?
Migrating to Fabric offers several advantages for developers. It simplifies deployment and infrastructure management, making it easier to build, test, and deploy applications across multiple environments. With its modular and extensible architecture, Fabric enables developers to automate repetitive tasks and workflows, freeing up time for innovation. Furthermore, Fabric promotes best practices for code management, ensuring consistency and repeatability across deployments.
What is Databricks migration?
Databricks migration refers to the process of moving your existing data, applications, and workloads from a different platform to Databricks. This involves migrating your data to Databricks' cloud storage, re-architecting your applications to leverage Databricks' capabilities, and ensuring seamless integration with your existing systems. It essentially helps you leverage the power of Databricks for your data analytics and machine learning needs.
Can Databricks be used as an ETL tool?
Absolutely! Databricks excels as an ETL (Extract, Transform, Load) tool. It combines powerful data processing capabilities with a unified platform, making it ideal for handling complex data pipelines. You can leverage its Spark engine for high-performance transformations, SQL for data manipulation, and built-in connectors for seamless integration with various data sources and sinks.
Is Microsoft Fabric a competitor to Snowflake?
While both Microsoft Fabric and Snowflake offer data warehousing and analytics capabilities, they differ significantly in their approach. Snowflake is a cloud-based data warehouse, while Fabric is a broader platform encompassing various data tools. Fabric aims to simplify data management within Microsoft's ecosystem, while Snowflake provides a dedicated and specialized data warehouse solution. Therefore, while both cater to similar needs, they are not direct competitors but rather offer alternative solutions depending on the specific requirements.
Is Databricks owned by Microsoft?
No, Databricks is not owned by Microsoft. Databricks is an independent company that provides a cloud-based platform for data engineering and analytics. While Microsoft offers Azure Databricks, a hosted version of Databricks' platform on Azure, Databricks itself remains an independent entity with its own development and operations.
Is Microsoft Fabric expensive?
Microsoft Fabric's pricing is based on a pay-as-you-go model, so the cost can vary depending on your specific usage and needs. It offers a free tier for experimentation, but you'll likely incur costs for larger workloads or advanced features like real-time analytics or machine learning. To get a precise estimate, it's best to contact Microsoft or utilize their pricing calculator to determine your tailored costs.
Is Microsoft fabric better than Databricks?
Choosing between Microsoft Fabric and Databricks depends on your specific needs and priorities. Fabric offers a more comprehensive, integrated platform with strong Azure integration, while Databricks boasts a mature, open-source foundation and a wider community. Ultimately, the best choice depends on your data scale, desired level of customization, and preferred ecosystem.
