For most of the tech people, the data engineering and data science projects are exciting by itself.
However, there should be a pretty good reason why to implement a project which may incur a significant capital cost.
To make the blog suggestions more practical I will attach the following tags to the posts to enable a more informed decision whether to proceed with the suggested solution.
How much does this solution cost
- maintenance / support
- indirect cost
- alternative cost
*TCO – total cost of ownership
How complex is to implement the solution or tip
- manhours needed to master the skill
- time to implement
- skill set required
- any obvious mistakes to avoid
I believe that sharing info on the mistakes is sometimes even more valuable than the plain vanilla solutions.
Let me be honest with you and uncover any silly “oops, I have missed that in documentation” style of errors and typos. Maybe some valuable points will escape your attention as well during the implementation of the solution.
Hopefully, it will save you some time and allow for making an estimate whether the analysed solution is mature enough.