Between Two Stacks: The Consequences of a Data-Less Decision

We've been having an ongoing debate in our team about what archicture to use to implement our new enterprise-level application. There are two possible solutions, one familiar, one fast.

We've been having an ongoing debate in our team about what archicture to use to implement our new enterprise-level application. There are two possible solutions, one familiar, one fast, but we can't seem to reach a conclusion as to which to use. A lack of applicable data is forcing us to make this key decision on intuition and guesswork, and I can't help but wonder how else we might be able to decide which path to take.

Familiarity vs Performance

Our new teammate Jerry, my boss Frank, and I have been kicking around ways to ensure that this new service will be blazing fast and thoroughly scalable, since much of our company's infrastructure will depend on it. Specifically, we're trying to determine the best (read: fastest) way of accessing the information in this system's database, since we believe that the amount of reads will be orders of magnitude larger than the amount of writes. It was partly for this reason that I benchmarked the performance of Entity Framework vs Dapper vs ADO.NET.

Throughout all of this, Jerry, Frank, and I have collectively tried to determine which assortment of technologies will allow the system to be both blazing fast and scalable, as well as not too different from what we already know. This, as you might imagine, is more difficult than we thought it would be.

There are two possible architectures we have bandied about. The first one is the one my group is most familiar with: the Microsoft stack of SQL Server, Entity Framework, and ASP.NET Web API. We build almost all of our other apps using this stack, and so development time would be much quicker if we use this setup.

best practices data analysis

The dataset also includes information on time and distance of flights which might also have an effect on delays. These columns can be analyzed with similar methods.

A lightweight desktop script tool is a must-have for data analysts. But how do you know which is the most suitable one?

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.

Visualization Best Practices for Data Scientists. Disclaimer: The ideas presented in this article are from the book: Story Telling With Data by Cole Nussbaumer Knaflic.

Tableau Data Analysis Tips and Tricks. Master the one of the most powerful data analytics tool with some handy shortcut and tricks.