Azure Analysis Services vs. Azure Synapse Analytics: What are the main differences?

By:   |   Updated: 2024-02-15   |   Comments   |   Related: > Azure


A particular e-commerce company would like to process its collected data to make its client's experience more delightful. One of the challenges is that the data is varied - it encompasses transaction histories, social media activities, and demographics. The company does not only require reports and visualizations to find out how customers behave but also wants prediction analytics for future trends.


In this case, the answer depends on the company-specific data analysis objectives.

However, if the main emphasis is placed on developing reports and visualizations based on data models that are far from simple, Azure Analysis Services would become a perfect solution, enabling easy connectivity with BI tools for interactive dashboards and effective performance of management matters.

Alternatively, if the company seeks to analyze data in real-time and perform predictive analytics, Azure Synapse Analytics will do just fine because its main purpose is managing large volumes of data and integrated tools for Predictive Analytics.

In this article, we will compare both, learn how they operate, and whether organizations should choose one over the other.

Overview of Azure Analysis Services and Azure Synapse Analytics

Azure Analysis Services is a cloud-based data analysis service hosted in Microsoft's platform known as Azure. It allows businesses to build enterprise-wide semantic data models that could also address BI issues. With the help of such models, data from different sources are transformed into a structured and understandable form, making it easier for analysis and reporting. However, Azure Analysis Services excels in scenarios of ad hoc analysis, interactive visualizations, and dashboards with tools such as Power BI or Excel, including other data visualization tools. Security, strong administration, and easy integration with other Azure services are the additional features of this service.

Microsoft Azure's integrated data analytics solution, formerly SQL Data Warehouse, is Azure Synapse Analytics. It amalgamates the features of enterprise data warehousing and big data analytics, which facilitate users to gather, store, process and analyze vast amounts of information using only one system. Azure Synapse Analytics has included features including massive scale querying, data integration capabilities for real-time analytics, and machine learning support. It is intended to support complex data analysis operations that require the integration of warehousing and advanced analytics such as machine learning and predictive analysis. Azure Synapse uses tools including Azure Data Factory, Azure Databricks, and Machine Learning to offer a comprehensive data analytic solution.

Data Volumes and Supported Sources

Azure Analysis Services is designed primarily to process in-memory-semantic data models, making it more adaptable for scenarios where users typically need interactive reports and analysis on smaller volumes of data than a data warehouse. Although it can handle voluminous models, it is designed to hold only a few petabytes of data. Azure Analysis Services supports many data sources and provides flexible data import into its in-memory model to guarantee optimal performance. This also applies to DirectQuery for real-time queries.

Azure Synapse is an evolution of Azure SQL Data Warehouse. This service is a cloud-based massively parallel processing (MPP) relational database designed to process and store large volumes of data on Azure. Several features have been added to make it one of the most powerful data analysis solutions on the market. Azure Synapse is a good choice for processing and analyzing large datasets since it can handle up to petabytes of data.

Users Concurrency

With its in-memory architecture and compression capabilities, Azure Analysis Services can serve more than a thousand users simultaneously. This offers response times, which makes it a number one alternative for businesses that need real-time interactive analytics and reporting multi-users.

Synapse, on the other hand, can handle 128 concurrent users from a single query point. It works better for companies that value performance over the number of users. Therefore, if your company needs an effective and efficient solution for a small group of users, Synapse is the answer.


Azure Analysis Services supports many features already built into SQL Server Analysis Services Enterprise Edition. It supports tabular models at compatibility levels 1200 and later. Tabular models are relational modeling constructs (model, tables, columns) articulated in tabular metadata object definitions in Tabular Model Scripting Language (TMSL) and template code, Tabular Object Model (TOM), partitions, perspectives, row-level security, two-way relationships, and translations are all supported.

Azure Synapse offers a high degree of integration with other Azure services such as Power BI, CosmosDB, and AzureML. Azure Synapse works with built-in cost analysis and alerts available at the Azure subscription level. It is a combination of these technologies:

  1. SQL technologies used in enterprise data warehousing.
  2. Spark technologies used for big data.
  3. ETL/ELT pipelines for data integration.

Pricing Consideration

Azure Analysis Services is priced using a pay-as-you-go model. Dedicated deployments are faster and more flexible but more expensive than standard deployment types, which provide lower performance and reduced scalability.  Costs vary by the Azure region in which the solution is deployed, and the compute level, number of CPUs, and memory resources are assigned to the deployment.  Azure Analysis Services is an adequate solution for a company that requires the development and publication of effective data models to be used with reporting tools.

Azure Synapse's pricing uses a consumption-based model, and the user must pay for what they used as it was computed units of storage and data transfer.  It is suited more toward organizations that need a tool for dealing with significant volumes of data from different sources.

Summary of Differences

The following table summarizes the differences between Azure Analysis Services and Azure Synapse Analytics in terms of managed data volumes, supported sources, number of users, and pricing:

  Azure Analysis Services Azure Synapse Analytics
Data volumetry Processing medium and large data models effectively.

It is suitable for BI analysis working with structured and semi-structured data.
Created to handle and evaluate data that is overly large, spanning from terabytes through petabytes.

Seamless high-speed processing technology for massive data.
Supported data sources Supports numerous sources: SQL databases, Excel files, cloud services, and live data streams. Comprehensive support for a wide range of sources: non-relational databases, data warehouses, data lakes, and streaming information.
User concurrency For cases where several individuals log in at the same time to view and analyze data models for reports. Created to facilitate high volumes of concurrent requests, suitable for situations with a lot of user concurrency.
Complexity Simpler, with a focus on data modeling and BI users. More sophisticated, featuring enhanced capabilities in data analysis with machine learning and real-time processing.
Pricing In accordance with a capacity-based pricing model, contingent on the required performance and memory. Pricing model based on resource consumption with data storage and computation fees that will increase depending upon usage.

Azure Analysis Services and Azure Synapse – A Possible Combination?

Azure Analysis Services and Azure Synapse Analytics, in combination with big data, can analyze large datasets and process complex queries. Both structures are MPP-based, giving excellent scalability and data processing capabilities. Azure Synapse Analytics is great at both data storage and processing, while other applications, such as the Analysis Services, are better for maintaining aggregates, thereby improving its data pipelining service.

This assortment is also superior in terms of efficiency and safety. Azure Synapse Analytics provides advanced analytical capabilities characterized by fast query processing and data transformation. This is critical for organizations that rely on instantaneous insight into their data sources. Conversely, Azure Analysis Services offers advanced security measures, such as row-level access security and dynamic data masking, that guarantee only authorized users can have the capability to access sensitive information.

In addition, both solutions can handle various data sources, such as relational databases, NoSQL databases, and many cloud storage services, so they prove to be quite flexible. These tools facilitate the development and sharing of interconnected data models that can be deployed without issues via different BI platforms like Power BI or Excel. This integration also ensures that data knowledge derived from such complexes is readily accessible for decision-making processes, which increases the business's intelligence.

Next Steps

After examining the differences and specific capabilities of Azure Analysis Services and Azure Synapse Analytics, it is essential to turn to an equally crucial aspect that the aggregate level fundamental services are a fusion and connection of such service elements as a whole that includes an ecosystem much beyond Azure such as Data Factory and their Integration Services that can be combined with Azure Data Lake and Power BI. We can examine how these new integrations might improve the experience associated with data analytics through effective and efficient workflows that will lead to continuous decisions of high quality based on such results. This will help us understand how Azure Analysis Services and Azure Synapse Analytics in the data plan focus on encompassing other tools.

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About the author
MSSQLTips author Amira Bedhiafi Amira Bedhiafi utilizes Azure Analysis Services to harness the power of scalable, cloud-based OLAP cubes for advanced data analysis and multidimensional modeling.

This author pledges the content of this article is based on professional experience and not AI generated.

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Article Last Updated: 2024-02-15

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