Comparison between Synapse Analytics and Azure SQL

pmscorca 792 Reputation points


I'd like to understand better the difference between Synapse Analytics and Azure SQL, supposing this scenario: managing 20-30-40 millions of data rows and not big data volumes, using some complex stored procedures to perform several calculations not aggregations, using some indexes if necessary.

For my little experience, it seems that Synapse is the best choice to move very large data volumes, but a data management solution isn't only composed of data movement operations.
It seems that the Synapse indexing offers lower advantages than a SQL Server.

Does a Synapse stored procedure offer the same good performances than a SQL Server stored procedure?

With the same workload, are Synapse and Azure SQL more performant than a SQL Server on-premise?

Synapse allows to use pipelines, but I can use Data Factory.

It seems that Synapse always spends a certain time to distribute data in the related architecture and to shuffle data to respond a query request. This time could be important when a limited data volume is handled than the total elapsed time.
It seems that Synapse isn't a more mature technology respect to a SQL Server Enterprise on-premise.
Any suggests to me, please?

Azure SQL Database
Azure Synapse Analytics
Azure Synapse Analytics
An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Previously known as Azure SQL Data Warehouse.
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  1. Marcin Policht 8,110 Reputation points MVP

    Understanding the differences between Azure Synapse Analytics and Azure SQL Database (and SQL Server) can help in choosing the right platform for your scenario:

    1. Data Volume and Scale: Azure Synapse Analytics is designed for processing large volumes of data and is well-suited for data warehousing and analytics workloads, especially with its integrated Apache Spark and SQL Server big data technologies. If you're dealing with very large data volumes, Synapse can handle it efficiently. Azure SQL Database is a fully managed relational database service and is suitable for transactional workloads and operational databases. It can also handle large datasets but might not be as optimized for analytics workloads as Synapse.
    2. Complex Calculations and Stored Procedures: Both Azure Synapse Analytics and Azure SQL Database support stored procedures for executing complex calculations. However, Synapse may not offer all the features and optimizations available in SQL Server for stored procedures, especially for very complex and resource-intensive operations. Azure SQL Database provides a familiar T-SQL programming environment with support for stored procedures, functions, and other database objects, making it suitable for a wide range of workload types.
    3. Performance: Performance comparisons between Azure Synapse Analytics, Azure SQL Database, and SQL Server on-premises depend on various factors, including workload characteristics, data volume, hardware configuration, and query optimization. In general, Azure Synapse Analytics and Azure SQL Database can offer excellent performance for cloud-native workloads, but there may be differences compared to SQL Server on-premises due to factors like network latency and resource allocation. Synapse's performance may be affected by data distribution and shuffling operations, especially for queries involving large datasets and complex joins. Optimizing data distribution, indexing, and query design can help improve performance.
    4. Maturity and Features: Azure SQL Database is a mature and fully managed database service with a wide range of features, including automatic backups, high availability, scalability, and security features. Azure Synapse Analytics is a relatively newer service that integrates data warehousing, big data analytics, and data integration capabilities. While it may not have the same level of maturity as SQL Server on-premises, it offers advanced analytics capabilities and integration with Azure services like Azure Data Factory and Azure Machine Learning.
    5. Data Movement and Integration: Azure Synapse Analytics includes built-in data integration capabilities for moving and transforming data, including data pipelines and data movement activities. While you can also use Azure Data Factory for data movement, Synapse provides tighter integration with its analytics and data warehousing features. Azure SQL Database can also integrate with Azure Data Factory and other Azure services for data movement and integration tasks.