An Overview of Azure Synapse: Understanding Synapse Vs Microsoft Fabric
In today’s rapidly evolving data landscape, a multifaceted approach to large-scale data analytics and storage remains a challenge for many business initiatives. Essentially, organisations require platforms to store large amounts of data while enabling instant data analysis for decision-making, as data volumes have been growing exponentially.
Microsoft Azure Synapse is a leading next-generation analytics platform that combines data integration, enterprise data warehousing, plus big data analytics into a unified platform. Let us first understand Azure Synapse, and then Synapse vs Microsoft Fabric.
What is Azure Synapse Analytics?
Many ask this question: is Azure Synapse a data warehouse? It’s more than that; it unifies data warehousing, big data analytics, and data integration. Azure SQL Data Warehouse was renamed Azure Synapse Analytics due to the addition of new capabilities.
Microsoft further added numerous other features over time, including serverless querying, Apache Spark integration, and real-time analytics. All of this transformed this PaaS (Platform as a Service) solution into a comprehensive analytics platform capable of handling any workload. Azure Synapse architecture combines SQL pools, Spark, and Data Explorer for hybrid data analytics.
With Synapse, businesses can design solutions that meet specific performance, scale, and security requirements. However, this flexibility comes with complexity, as businesses must manage provisioning, integration, and scaling across multiple services.
In a nutshell, Azure Synapse is an easy-to-use data management platform with all-in-one capabilities for ingesting, preparing, querying, and visualising data. It streamlines analytics workflows without requiring stakeholders to juggle additional tools.
Core Features of Azure Synapse : Unified Analytics and Scalability
Unified Analytics Platform:
It combines big data analytics and enterprise data warehousing on a single platform. This enables users to run limitless analytics in Azure Synapse without switching between tools or services.
Secure and Scalable:
Azure Synapse supports independent scaling of compute and storage, enabling business owners to pay only for what they require. Data is secured through encryption, firewall rules, VNET service endpoints, role-based access controls, etc.
Data Integration:
Azure Synapse enables the ingestion and transformation of data from various sources, including SQL databases, Cosmos DB, and Blob Storage. This helps with flow control over intricate ETL (Extract, Transform, Load) workflows, which are critical for contemporary analytics.
Advanced Analytics and ML:
With built-in integration for Azure Machine Learning and Spark, Synapse enables operators to build, train, and deploy machine learning models directly on their data, providing seamless AI-driven insights.
A Note on Microsoft Fabric:
Microsoft Fabric is a software-as-a-service (SaaS) approach that brings everything together – data ingestion, engineering, warehousing, real-time analytics, and business intelligence into a single platform. It leverages technologies from Synapse, Power BI, and Azure Data Factory, and renders a cohesive experience with shared governance, compute, and storage through Microsoft OneLake, the OneDrive for data.
Fabric does not require separate compute resources or the management of integrations; instead, it is pre-wired, accessible via a familiar UI, and secured with Microsoft 365. It is a much simpler experience with broader reach, designed to serve all stakeholders data engineers, analysts, and business users in one environment.
Microsoft Azure Synapse Vs Microsoft Fabric:
Microsoft Azure Synapse has been a powerhouse in data warehousing and analytics, enabling businesses to process large datasets. However, amid business demands for agility and the advent of AI, Microsoft Fabric has made its mark as a next-generation data platform that unifies and rationalises data management with an all-new approach. Let us compare the two platforms now with this in mind.
| Feature | Microsoft Fabric (SaaS) | Azure Synapse (PaaS) |
| Architecture | Unified platform built around OneLake with shared capacity units for all workloads | Modular platform combining SQL pools, Spark clusters, and Data Explorer, using ADLS Gen2 for storage |
| Infrastructure | Microsoft handles infrastructure, scaling, and updates automatically | User manages deployment, scaling, and configuration |
| Data Integration | Fully unified data factory with automatic integration and many connectors | Uses Synapse Pipelines (based on Azure Data Factory), requires explicit linked service setup |
| Analytics workloads | Unified experience with SQL, Spark, Power BI, Real-time analytics, and built-in AI Copilot | Modular workloads, including SQL Analytics, Spark, Data Explorer, and AI/ML, are supported but not fully integrated. |
| Real-time analytics | Native support with Real-Time Hub and Data Activator for alerts and triggers | Primarily batch processing with some streaming support through Data Explorer. |
| AI & ML Integration | Deep AI/ML integration with Copilot AI assistants across workloads | Uses Azure ML and OpenAI services, but AI is less unified. |
| Security & Governance | Built-in automated governance with Microsoft Purview, secured OneLake storage, and simplified role management. | Requires manual integration and management of security, and uses Microsoft Purview separately |
| Cost | Pay per Fabric Capacity Unit (shared across services) | Costs based on individual compute resources and storage |
| User Interface | Fabric portal tailored to user personas like data scientists, engineers, and BI analysts | Synapse Studio is a central management and development environment. |
Let us compare the disadvantages specifically now.
| Feature | Azure Synapse | Microsoft Fabric |
| Architecture | Complex and fragmented; requires multiple services to manage different workloads, leading to integration and maintenance challenges. | Truly unified architecture combining data lakes, warehouses, and streaming into one cohesive platform, eliminating complexity. |
| Real-time data processing | Limited real-time processing capabilities; mostly batch-oriented, which restricts responsiveness and agility. | Native real-time data processing and streaming with built-in tools for continuous analytics and alerts. |
| Costs | Costs escalate significantly as workloads scale, driven by resource provisioning and management. | Smarter cost efficiency through a flexible pay-as-you-go model that aligns spending with actual usage. |
| AI & ML | Supports AI via additional tools; lacks integrated, built-in AI capabilities, creating innovation barriers. | AI embedded at the core, offering out-of-the-box predictive analytics, automation, and ML for quick insights. |
| Data Governance & Security | Requires manual setup for governance, security, and compliance, increasing administrative burden. | Advanced built-in security and governance controls, simplifying compliance and data protection. |
| Performance | Potential bottlenecks in complex queries impact smooth analytics and decision-making. | Designed for high performance with optimised data storage and compute, supporting advanced analytics seamlessly. |
| User Experience | Modular user environments require multiple tools and configurations. | Unified user interface integrating all data workloads for streamlined management and operation. |
Choose Kloudify as Your Consulting and Implementation Partner:
Both Microsoft Fabric and Azure Synapse aim to solve the same fundamental challenge for businesses: helping transform raw data into actionable insights. The way both platforms step up to solve the problem, however, is fundamentally different.
Choosing the right data platform is critical for businesses that aim to stay competitive in an AI-driven world. Microsoft Fabric definitely stands out as the next-generation solution, offering AI-powered insights, effortless scalability, and cost-efficient analytics.
Azure Synapse and Azure SQL are two reliable platforms; Microsoft Fabric’s unified ecosystem reshapes enterprise data management, especially as AI and ML increasingly influence technology. Are you in a fix? Wondering your way forward? Team Kloudify is here to help.
