Question

Assuming a fairly standard business analytics context, if you had to make a choice between the Azure suite (Data Lake, Service Bus, Batch, Data Factory, Data Bricks, Stream Analytics etc) and the corresponding AWS suite (Glue, Athena, Step-Functions, Kinesis, Firehose, SQS etc) , for supporting data mining and analytics, which would you go for and why?

Answer 1

I prefer Azure suite rather than AWS suite.

The reasons why I feel Azure suite is better are as follows:

1. Azure is cheaper than AWS.

2. It also offers some extra properties which makes it better than AWS.

3. The cli works perfectly, all the time, without any cryptic failures.

4. Azure gives stronger and faster PaaS capabilities which nowadays is more important part of Cloud infrastructure.

5. With Azure PaaS, much of the infrastructure management is taken care of behind the scenes by Microsoft.

6. Azure now brings to the array an integrated environment for testing, developing, and deploying Cloud apps.

7. The client has the choice of frameworks, and open development languages promotes the flexibility for Azure migration, whereas AWS is widely perceived as being complicated.

8. The new design of Azure is based on Security Development Lifecycle (SDL) which is an industry’s major assurance process.

9. Azure and AWS have slightly different approach, when it comes to developer tools. Only based on the processes and tools that is used by Amazon's own internal engineering teams, the AWS suite of Developer Tools mainly focuses on supporting DevOps.

10. If an organization uses Microsoft software, then it surely has an ‘Enterprise Agreement’ with Microsoft. It is titled to receive discounts on the Microsoft software being used as Microsoft normally squeeze these agreements to lower the pricing of Azure. Thus, with the enterprise agreement, enterprises can typically obtain significant incentives for using Azure.