Azure Analysis Services vs. Azure Synapse Analytics

Microsoft Azure Analysis Services is best tool which is well suited for many type of scenarios. Like if the organization is dealing with a lot of critical data and need some better analysis and insights for that data then tool serves the best. It helps in depth analysis and getting the desired result which helps in making big decision for any organization. We can create role based access for sensitive data hence it is very helpful for security point of view. Helps in making the business more productive and taking decision based on facts. It is less appropriate for scenarios like where data amount is less and the solution is very costly and someone can get a cheaper solution. Also not suited for environment where user directory do not exist because without the help of user directory role could not be created hence proper utilization of this tool will not be possible.

Incentivized

In terms of a well-suited scenario - the Azure Synapse can be used to capture data from multiple sources (especially from onPrem sources apart from Dataverse) and update the transformed data based on the given conditions (eg: refresh data based on the specified date/time ranges). Also, the transformed data can simply be transferred to Azure Data Lake for further processing by utilizing other analytics tools such as PowerBI.

Incentivized

• You can import data from various sources (i.e. Oracle, Azure SQL...)
• You can store data models
• You can elaborate data and create insights
• Perfect integration with Excel and Power BI

Incentivized

• The combination of SQL/unstructured data
• Keeping things "complicated, but simple"; [heterogeneous] data formats seen as just SQL tables to business experts used to use Power BI, Excel, and any other traditional SQL-oriented BI tools
• Integration options using "Synapse pipelines", the application of ADFs
• The greatly integrated solution of independent things (Spark MPP cluster, MPP SQL Servers, ADFs) - all sitting under one roof. Great job!
• Integration with super-fast, globally replicated data. I really appreciate the integration of NoSQL databases (namely Core API and Mongo API under Cosmos DB) with purely batch-processed BI data

Incentivized

• Microsoft Azure Analysis Services is very costly solution and in that price we can get some better business intelligence tool with lot more of capabilities
• The dashboard or we can say user interface is complex and need time to understand and gain expertise in order for proper working.
• It needs continuation monitoring which is sometime a big task.
• Sometime, the tool shows unusual behavior and become unstable, so we need to clear temp files for proper functioning.

Incentivized

• With Azure, it's always the same issue, too many moving parts doing similar things with no specialisation. ADF, Fabric Data Factory and Synapse pipeline serve the same purpose. Same goes for Fabric Warehouse and Synapse SQL pools.
• Could do better with serverless workloads considering the competition from databricks and its own fabric warehouse
• Synapse pipelines is a replica of Azure Data Factory with no tight integration with Synapse and to a surprise, with missing features from ADF. Integration of warehouse can be improved with in environment ETl tools

Incentivized

The data warehouse portion is very much like old style on-prem SQL server, so most SQL skills one has mastered carry over easily. Azure Data Factory has an easy drag and drop system which allows quick building of pipelines with minimal coding. The Spark portion is the only really complex portion, but if there's an in-house python expert, then the Spark portion is also quiet useable.

Incentivized

Microsoft does its best to support Synapse. More and more articles are being added to the documentation, providing more useful information on best utilizing its features. The examples provided work well for basic knowledge, but more complex examples should be added to further assist in discovering the vast abilities that the system has.

Incentivized

We have used the IBM cloud which was truly a specific nightmare for our team. User experience, layout, and design is big for us as it understandably is with many people. Even if any type of program can technically do all that we need it to, we still found our team will not be as motivated or satisfied using it compared to something more visually appealing and smooth.

Incentivized

They're all part of the Microsoft Azure family, so they are not exactly competitors. They overlap in functionality, but they're targeted at different levels of customers. Azure Data Factory is an excellent stand-alone PaaS (included in Synapse Analytics) for writing, scheduling, and monitoring pipelines. Azure SQL Database (and all the Azure SQL family) is excellent for traditional, SQL-based data warehouses, especially if you're migrating from on-premises. Combined with Azure Data Factory (that can run SSIS packages), it's a perfect solution for a simple path to the cloud. Azure Databricks is effectively the only internal "competitor" to Synapse Analytics but targeted more to a "platform-agnostic" audience. On the other hand, Synapse is more of a proprietary mix of products that are more tightly related to Microsoft technologies.

Incentivized

• Website analytics; We have realized so many benefits like traffic analysis, CRM, click-through funnels, and many more.
• Aas allows us to do complex calculations and aggregations at slight efforts. It accommodates DAX expression, which in return is simple to apply.

Incentivized

• It definitely has a positive impact on ROI. We are able to use it to generate MORE revenue through predictive analytics and pricing optimization.
• Because of the SQL Data Warehouse design, we're able to set up some self service reporting tools which allow our users to generate reports ad hoc instead of having a full time employee creating these by hand.
• Having visibility into the data is very useful for management to make good business decisions.

Incentivized