Beautiful Lies – in the good case charts can be unclear due to too much data, no distinct colors, missing labels etc. in a worse case it can be miss leading. Few tips and example of such –
http://flowingdata.com/2015/08/11/real-chart-rules-to-follow
RDBMS under the hood – a big part of this post deals with complexity and data structures. This introduction provides the ground to the rest of the post about different processes in the RDBMS, their costs and different optimizations. While I learned a big part of the post content as an undergraduate I wish I would also learn the more interesting part about optimizations.
http://coding-geek.com/how-databases-work/
Scaling and sharding @ pinterest – another link to a post about RDBMS. RDBMS is sometimes considered less sexy comparing to NoSQL, bigdata fancy solutions. This post explains why pinterest chose MySQL as their data warehouse solution and how they scale it. Comparing to the mambo jambo that happen in many companies the fact that this solution works for the for 3 years now is amazing.
https://engineering.pinterest.com/blog/sharding-pinterest-how-we-scaled-our-mysql-fleet/
What python cannot do – I was actually very optimistic about this post but ended up a bit disappointed. Some of the points it points on are relevant and the other I find less relevant. By less relevant points I refer to other programming languages with the same disadvantages – java and issues with versions. Several python modules lack good support – really? everywhere there is an open source (and also where there are commercial packages) documentation and quality are never perfect. “Errors can be identified only on run time” – dynamic typing can be both a pro and a con of python, it is a feature of the language. “Slower than other compiled languages” – this is miss leading as python is not compiled and comparing it C++ and C is weird.
http://www.allaboutweb.biz/what-is-it-that-python-cannot-do/
http://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures
Tom Ron 