Data warehouse in Azure Synapse vs. Fabric

Question

What are the key considerations when designing a data warehouse in Azure Synapse vs. Fabric?

Tuhin Paul · Answer

Designing a data warehouse in Azure Synapse versus Microsoft Fabric involves understanding the strengths use cases and architectural differences of each platform. Azure Synapse Analytics Purpose : A unified analytics platform that integrates big data and data warehousing. Core Features : SQL-based data warehousing (dedicated SQL pools). Apache Spark integration for big data processing. Data integration pipelines (Synapse Pipelines). Built-in support for hybrid transactional/analytical processing (HTAP). Advanced analytics with machine learning integration. Use Cases : Large-scale enterprise data warehousing real-time analytics and big data processing. Microsoft Fabric Purpose : An end-to-end analytics platform that unifies data engineering data warehousing data integration and business intelligence. Core Features : Built on OneLake  a unified data lake for all analytics workloads. Integration with Power BI for seamless reporting and visualization. Simplified data engineering and warehousing with Data Warehouse and Data Engineering experiences. Real-time analytics and AI/ML capabilities. Use Cases : Modern data warehousing self-service analytics and unified data management. When to Choose Azure Synapse vs. Microsoft Fabric Choose Azure Synapse If : You need fine-grained control over data warehousing and big data processing. You have complex integration requirements with hybrid data sources. You are running large-scale high-performance workloads . You already have significant investments in Azure services. Choose Microsoft Fabric If : You want a unified end-to-end analytics platform . You prioritize ease of use and rapid deployment . You need seamless integration with Power BI for reporting. You prefer a simplified automated approach to data management.