Build A Data Stack That Lasts - How To Ensure Your Data Infrastructure Is Maintainable

As a consultant, I have been called in to review and, in many cases, replace dozens of half-finished, abandoned, and sometimes forgotten data infrastructure projects.

The data infrastructure in a few cases may just need a little tweaking to operate effectively, but other times the project is either so incomplete or so lacking in a central design that the best thing to do is replace the old system.

Trust me, I’d love it if I could come into a project and simply change a few lines of code, and then everything would just work. However, so many projects are filled with unclear design decisions or resume-driven development that were never rooted in good planning.

Of course, business stakeholders may have also push to get things done quickly. Forcing data teams to take on tech debt that will never be fixed. Don’t get me wrong, you want to get things done and move projects forward. But taking on technical debt is a decision that needs to be made intentionally. Otherwise, like in resume driven development, your data infrastructure might disappear.

This begs the question.

How do you ensure the data infrastructure you’re building doesn’t get replaced as soon as you leave in the future?

In this article I wanted to dive into the problems I often come into that require me to replace the current data infrastructure and how you can avoid it.

So let’s dive in.
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Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio

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About me:
I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

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