Azure Analysis Services vs. Azure Data Factory

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 a data pipeline, you will be able to add different kinds of activities for example connect from your on-premise SFTP and move CSV files to storage accounts. As well data factory has its own data flow if you are an ETL developer who experimented with maybe you have worked with SSIS, thus, you will start quickly with this new feature of the data factory.

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

• Creating ETL and ELT workflows as well as orchestrating and monitoring pipelines without writing any code.
• Hybrid data integration is easily and agilely possible through this software.
• It has lot of various useful components

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

• Learning curve for pipeline creation interface.
• Alerting isn't necessarily built in. Had to work around this to meet team needs.
• With GIT enabled, some features can only be done via git, while some need to be done via the portal.

Incentivized

So far product has performed as expected. We were noticing some performance issues, but they were largely Synapse related. This has led to a shift from Synapse to Databricks. Overall this has delayed our analytic platform. Once databricks becomes fully operational, Azure Data Factory will be critical to our environment and future success.

Incentivized

We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue

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

Azure Data Factory fits well into our overall systems architecture where we already utilize largely Azure services and also Microsoft based products in the on-premises environment. I think cost structure is also very competitive with Azure Data Factory. Most services provide a visual interface for designing ETL workflows, but our team found Azure Data Factory's interface more intuitive.

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

• Limiting the amount of data moving up and down from the cloud for cloud-native applications.
• Overall simple to use interface which is actually easier for a first time ETL developer than SSIS.

Incentivized