Azure Analysis Services vs. Azure Data Factory

We would have many technical issues and glitches with previous similar providers but found that Azure Analysis Services can simply handle our workload and memory better. I remember we lost an account due to cloud issues not fully saving or corrupting some files. Granted, this is rare with any cloud but haven't had that issue with the same load of memory with Azure Analysis Services.

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Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.

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• Providing role based access or we can say privilege based on the role to the user if it is integrated with Azure active directory and hence securing the access to sensitive data.
• We use to run different type of analytics services to get the better result which is hectic if done manually or with human efforts.
• We also use to collect bulk of data with the help of this tool and run customized test cases for better efficiency of result and better decision making. The result are very crucial and helps in taking big decision.
• It supports different or we can say heterogeneous database vendors like the Oracle, SQL, and hence make the task easy.

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• It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
• We can use linked service in multiple pipeline/data load.
• It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.

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• There is very least content available on Microsoft docs and the Internet to get through with AAS.
• i faced lots of connection issues while implementation.
• The need for serious platform experience or expertise.

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• Limited source/sink (target) connectors depending on which area of Azure Data Factory you are using.
• Does not yet have parity with SSIS as far as the transforms available.

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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.

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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

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The platform has vast number of features and modules. The UI is sleek and once you get to use to it, you will be able to do a lot of stuff. Also support for data sources is more in Azure Analysis Services.

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The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.

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• The tool has helped us a lot for taking critical decision which has resulted in the increase of profit of organization by 18 percent.
• Return on investment is 70 percent as we are unable to utilize the tool with full functionalities, so we are still testing some of the use cases.
• Human effort has been reduced and task has become automated hence helped in cost management

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• It is very useful and make things easier
• Debugging can improve
• Its better suited than other products with the same objective

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