Plurrrr

Vectorization in Python, as implemented by NumPy, can give you faster operations by using fast, low-level code to operate on bulk data. And Pandas builds on NumPy to provide similarly fast functionality. But vectorization isn’t a magic bullet that will solve all your problems: sometimes it will come at the cost of higher memory usage, sometimes the operation you need isn’t supported, and sometimes it’s just not relevant.

Y’know that situation where you tell the client, “Here’s your website and you can edit those four (4) little homepage features in the CMS” and the client says “Okay okay okay” and you check the site a week later and it looks bad because the client —despite your incredible documentation— put an odd number of items in the feature grid? It’s a major minor problem that’s tough to explain to the client, but it all comes down to…

The dangler.

Many organizations that adopt Docker or an adjacent containerization technology find it increases efficiency and accelerates the development process. Docker’s not something that magically improves every system though. In this article, we’ll look at some scenarios where moving to containers might be more of a hindrance than a help.