Navigating the Data Platform Landscape: Finding the Perfect Fit for Your Company Azure Synapse OR Microsoft Fabric
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Microsoft Fabric represents a cloud-centric Software as a Service (SaaS) solution that amalgamates an array of indispensable data and analytics instruments requisite for enterprises. This comprehensive ensemble encompasses pivotal components such as Data Factory, Synapse Data Warehouse, Synapse Data Engineering, Synapse Data Science, Synapse Real-Time Analytics, Power BI, and Data Activator.
Microsoft Fabric is built upon an open, lake-centric design, featuring a central, multi-cloud repository known as OneLake. This platform combines the strengths of data mesh, data fabric, and data hub to provide a comprehensive solution for data integration, data engineering, real-time analytics, data science, and business intelligence requirements, all while ensuring the privacy and security of data.
| Components of Microsoft Fabric | |
| Data Engineering | Notebook-driven tool for transforming data at scale using Spark. |
| Data Factory | Tool for data engineers to build integrations from more than 200 native connectors. Combines the simplicity of PowerQuery with the scale of Azure Data Factory. |
| Data Science | Experience to build, deploy, and operationalize ML models integrated with Azure Machine Learning. |
| Data Warehouse | Delta Lake columnar storage engine for data providing a SQL endpoint, fully separating compute from storage. |
| Real Time Analytics | Experience in streaming data (often unstructured or semi-structured) from an Azure event hub and storage in KQL databases. |
| Power BI | Experience in Data analytics and data visualization. |
| Data Activator | No code platform to detect actionable conditions and trigger actions (think Teams messages, emails, PowerAutomate tasks). |
| FAQ’s |
| If you have existing Power BI workspaces - You can turn on a Fabric capacity, no problem. There is no migration involved there. Your content still lives in the workspace, and you have those additional experiences at your disposal. Power BI still offers the same capabilities as before - you can build datasets using import or DirectQuery, build reports, publish reports, etc. Power BI Desktop is also not going anywhere. The Power BI experience won’t fundamentally change. |
| If you have Power BI projects in flight or planned for this year - You are not obligated to change course to align with the new Fabric paradigms. However, now that you have these new features available in your toolkit, you may want to consider them as potential options for the future. |
| If you’re using ADF or Synapse - There is currently no direct or automatic path for migration. ADF pipelines cannot be migrated directly to Fabric, although this is a planned feature on the roadmap. Synapse Spark workloads, however, can be easily migrated. It's important to note that the underlying data still needs to be connected to OneLake. The key differentiation between Azure Synapse and Microsoft Fabric's capabilities is that Azure Synapse is a PaaS (Platform as a Service), whereas Microsoft Fabric is a SaaS (Software as a Service). Consequently, transitioning from Synapse to Fabric will involve fundamentally different processes. Fabric is positioned as the successor to ADF and Azure Synapse, intended to enhance the user experience from those platforms. This eliminates the necessity of managing multiple Azure resources. The provision of Spark clusters will result in a significantly faster and more streamlined experience. The compute resources you acquire are pooled with your Power BI resources. This might be viewed as advantageous by some, but it could also be seen as a drawback by others who believe it could lead to an increase in the overall cost of ownership. |
| Can I integrate my existing workloads from Synapse to Microsoft Fabric - Currently, there is no automated way to upgrade existing Synapse workloads. They must be manually migrated by adjusting the notebooks, SQL scripts, pipelines, etc |
| What does Fabric add to Power BI - Microsoft Fabric enhances the overall developer experience, particularly with its Git integration features. The long-standing pain point for Power BI developers has been the inability to use Git for .pbix files, which are stored as compressed binary files. Microsoft Fabric is addressing this issue by introducing more source control capabilities to workspaces. These workspaces can now be linked with Azure DevOps, allowing the underlying data models and reports to be accessed as JSON files within a repository. As a result, Power BI development is aligning more closely with software development paradigms. Models and reports can be compared using diffs, merged, and changes can be approved through Pull Requests. |
| Can Microsoft Fabric be used on-premises - As of now, Microsoft Fabric is a SaaS cloud-based offering. |
| Azure Synapse vs Microsoft Fabric | ||
| Service Type | Platform as a Service (PaaS) | Software as a Service (SaaS) |
| Architecture | The Azure Synapse architecture consists of several components, including Azure Storage, Control Node, Compute Nodes, Data Movement Service, Distributions, Hash-Distributed Tables, Round-Robin Distributed Tables, and Replicated Tables. | The Microsoft Fabric architecture consists of seven workloads that operate on top of OneLake: Data Factory, Synapse Data Warehousing, Synapse Data Engineering, Synapse Data Science, Synapse Real-Time Analytics, Power BI, and Data Activator. |
| Warehouse | SQL Serverless. | SQL Endpoint. |
| SQL Dedicated (SQL Data Warehouse). | Warehouse. | |
| Spark Session | Apache Spark Pools. | Managed Spark Pools. |
| Apache Spark Notebooks. | Notebook. | |
| Apache Spark Jobs. | Spark Job Definition. | |
| Spark infrastructure | Time to spin up 2-3 Minutes. | Generally, it is between 20 and 30 seconds. |
| Database & Query Language | Data Explorer (KQL Scripts). | KQL Queryset. |
| Data Explorer Database. | KQL Database. | |
| Synapse Link | Azure Synapse Link provides a fully managed and turnkey experience for landing operational data in Azure Synapse Analytics dedicated SQL pools. | Synapse Link for Dataverse provides direct integration with Microsoft Fabric. |
| Tools | Synapse Studio. | “Data Engineering”, “Data Science”, “Data Warehousing” and “Real-time Analytics”; Power BI, Data Factory and Reflex as stand-alone tools. |
| Code Integration | Source code control integration is present | Source control using its native syntax makes it much easier to track and review changes. |
| Data Science | ML / MLOps. | Data Science. |
| MPL | Mapping Data Flows are employed for the ingestion and transformation of data in Azure Synapse. | Not supported by Microsoft Fabric. |
| Pipeline | Azure Synapse allows you to collect, transform, and analyze data from just one platform. | Microsoft Fabric includes Data Factory capabilities, including the ability to create pipelines that orchestrate data ingestion and transformation tasks. |
| Collaboration | The Notebooks experience offers some attractive features, such as comments and co-editing. | Notebooks experience offers some attractive features (e.g. comments, co-editing) for larger teams by boosting collaboration and reducing hand-offs. |
| User controls | Data of the application can be managed by the customer. | Everything is managed by Microsoft. |
| Access | Gets access to Synapse Studio for pipeline creation and runtime access for deployment. | All users get access including the end user. |
| Scale | Admins must change the configuration to scale. | Scale up or down the processing power as we need. |
| Security | Role-based access control (RBAC) to manage different aspects of Synapse Studio. Also, row/column/object level security is provided. | Hosted on Microsoft cloud infrastructure. OneSecurity layer manages access across all Fabric objects. |
| Licensing | Serverless billing option. 1 to 3-year reserved instance licenses available for dedicated pools. | PAYGO option without long-term commitment, Reserved Instances can save money. On-demand scale-up and down feature saves money. |
| Advantages of Microsoft Fabric as an Integrated SaaS | |
| Simplicity | With Fabric, simplicity is key. From quick sign-up to rapid deployment, users can experience actual business benefits within minutes. There’s no need to worry about infrastructure procurement, setup, or maintenance. All the features and functions of Microsoft Fabric are conveniently accessible through a single web portal. |
| Completeness | Microsoft Fabric fulfills all of your analytics needs by offering end-to-end capabilities. From data integration and modeling to visualization, analysis, and AI/ML operations, Fabric empowers you to make data-driven decisions and scientific forecasts, thereby providing your organization with a competitive edge. |
| Collaboration | Microsoft Fabric facilitates seamless collaboration among teams involved in the analytics process. Data engineers, data warehousing experts, data scientists, analysts, and business users can easily work together on the same platform, sharing insights, data, and best practices. |
| Governance | With Microsoft Fabric, you can establish a single, reliable source of truth. Unified governance features enable you to control data quality, data security, privacy, compliance, and access, ensuring consistent and reliable data governance across your organization. |
| Innovation | Microsoft Fabric keeps you at the forefront of innovation, leveraging the latest advancements from Microsoft and its partners. With built-in generative AI and language model services like Copilot, you can create groundbreaking AI experiences that you’d have to provision and set up separately in a PaaS environment. |
| Openness | OneLake serves as the primary data lake, based on open formats like Parquet, and including interfaces with other cloud services, like Amazon S3. |
In conclusion - There is no straightforward best choice between Microsoft Fabric and Azure Synapse as they cater to different needs. It's important to carefully evaluate organization's requirements and consider factors such as data processing needs, application architecture, scalability requirements, and existing tooling before making a decision.
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