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Can someone explain what is the difference between Azure Analysis Services and Azure Synapse Analytics? Why would one use Analysis Services over Synapse Analytics?

Thanks,

Raju Joseph
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2 Answers2

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Azure Synapse Analytics is a rebrand of Azure SQL Data Warehouse (GA) with additional Analytics/Streaming/ML enhancements (currently at Public Preview).
here

Azure Analysis Services (AAS) is the Azure PaaS version for SQL Server Analysis Services.
here.

Please note -

"Power BI Premium is the focus for enterprise BI and the primary target for future investments. In time, Power BI Premium will provide a superset of the capabilities when compared to Azure Analysis Services."
here

David דודו Markovitz
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    Thanks. Can you share a use case where I would need both services? Would one use Azure Analysis Services on top of Synapse Analytics as an additional layer for tools like Power BI to consume? – Raju Joseph Mar 13 '20 at 13:22
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    The classic Microsoft on-prem architecture was SQL Server (DWH) + SQL Server Analysis Services. + Power BI. You could have copy-paste this architecture to Azure using Azure SQL / Azure SQL Managed Instance / Azure SQL DWH aka Azure Synapse Analytics + Azure Analysis Services + Power BI. As I mentioned, the Power BI product group direction is to include the AAS capabilities as part of Power BI. – David דודו Markovitz Mar 13 '20 at 13:40
  • Thanks, makes sense now. – Raju Joseph Mar 13 '20 at 13:45
  • Synapse also has an Apache Spark engine, so can potentially be a mechanism to migrate or blend the SQL Server world with the world of "Big Data". As well as access the myriad of ML models available in the Spark Universe. – SinisterPenguin Jan 18 '22 at 08:46
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Azure Analysis Services - can be used

  • when small volume of data is to be analyzed,
  • for detailed analysis,
  • to form dashboard development,
  • when concurrency required is high (thousands of users)

Azure Synapse Analytics - can be used

  • when very high volumes of data is to be analyzed,
  • when analysis involves complex queries,
  • when concurrency required is low (128 users or few)
  • for data mining.
Karan Bhatia
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