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Part 2 - Overengineering of a cloud application

In the last post we looked over a cloud solution design to ingest small CSV files uploaded by users. This files were crunched by the system that would generate static reports based on the content. Nothing fancy or complex.
The NFR requirements are light, because the real business value stays in the generated reports:

  • Under 200 users worldwide 
  • Concurrency level is 10% (20 users online simultan) 
  • Less than 15 CSV uploaded in total per day 
  • Basic reporting functionality 
  • Current DB size 150MB (2M reporting entries) 
  • DB forecast for next 3 years is 1GB (20-25M reporting entries) 
  • CSV has up to 1000 entries (maximum 10 columns)
The system that was design for this application was a state of the art system - salable, robust, containing all the current technology trends. But of course was over engineering, to powerful and to expensive.
Now, the biggest concern was how we can reduce the running cost of the system with a minimal impact (development cost). One of the drivers was that we had to come up with a solution that would keep the monthly cost under a specific threshold and also keeping the development cost limited to a specific number of days - rewriting the system was out of the context.

The changes that were done to the system to reduce running cost with a development budget constraints were:
  1. Remove Azure HDInsight - there was no need such a powerful system to analyze 15 CSVs/day (under 15MB)
  2. Remove DocumentDB - reporting data for charts can be stored anywhere. For reports that are static, with a low number of points where updates are happening not very often any kind of storage is perfect. 
  3. Add Power BI - Used as a service it is hanly and useful solution for data visualization. There is full support for CSV files. Once you import them to Power BI you can generate any report on top of them. For 200 users there is no need for 200 Power BI users. The reports can be embedded to a web page. We only have 10 Power BI users in total (5 real users, 3 for pre-production, 2 for testing) that will be reduced to 6 in the near future.
  4. OneDrive - The client already had Office 356 subscription activated, it was a perfect location to store data and fetch them to Power BI.
  5. Remove Azure SQL - There is no need for Azure SQL anymore.
  6. Use Azure AD for user management - The client already user Office 356 an had their location AD federated.
  7. Remove Azure Service Bus - I'm a big fan of messaging system and ESB, but for this context there was no real need. Azure Service Fabric offers internal some 'internal' collections that can replace ESB for this specific case.
  8. Remove Reporting Service - There is no need for this service anymore, Power BI has full support.
  9. Remove CVS Pusher Service - There is no need for such a service. CSVs are uploaded directly to OneDrive and ingested based on their locations
  10. Remove UserMng Service - No need for such a service anymore. IT manage users access based on AD roles and groups.
The running cost were reduced with more than 60% (most of the cost was generated by Azure HDInsight that was a to powerful solution for this kind of 'airplane'. 
We can write a system in multiple ways, using different services and components. The thing that we need to remember that in the end we need to resolve a business problem using a limited number of resources. 

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