Azure Analysis Services vs. Azure Data Lake Analytics

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.

Incentivized

Azure Data Lake Analytics services are beneficial when working with a lot of data. It can process enormous amounts of data extremely quickly. Service is secure and easy to set up, build, scale, and run on Azure. Regarding big data analytics and reporting, parallel processing has a significant impact. It consolidated our analytics from multiple systems and increased our analysis productivity. This tool has excellent support for reporting tools like Power BI and is very quick when performing analytics.

Incentivized

• 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.

Incentivized

• Process large data transformation jobs using pretty much any language needed.
• Native integration with Azure storage.
• Top notch security that fulfills all audit needs.
• Easy to consolidate enterprise data under one location - Single source of truth.

Incentivized

• 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.

Incentivized

• There's a bit of bias towards cloud with ADL Analytics. Depending upon a company's infra strategy and investment plans, there are some challenges with migration and integeration.
• Not worth the time/effort/money if the organization doesn't have "Volume" of data. Cost effective only when daily loads exceed around 1million.
• While training materials are available online, Adoption rate - Yet to pick up.

Incentivized

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.

Incentivized

We did some research about Alibaba Cloud Data Lake Analytics and even being cheaper than Azure Data Lake Analytics, we decided to go for the second one once we noticed they have more features and better documentation. Another thing we considered during this process was the fact that we have more people that already have Azure Cloud knowledge.

Incentivized

• 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

Incentivized

• It lets us manage and scan data, making our work easy and efficient.
• It helped me manage real-time data, process it, and send it to reporting.
• Data centralization or data warehousing projects are being implemented with its help.

Incentivized