Working with the raw data has lots of benefits, since at the point of ingest you don’t know all of the possible uses for the data. If you rationalise that data down to just the set of fields and/or aggregate it up to fit just a specific use case then you lose the fidelity of the data that could be useful elsewhere. This is one of the premises and benefits of a data lake done well.

propelled by a “water plasma” engine. Solar panels generate electrical power, which the vehicle then uses to generate microwaves, which superheat the water up to Sun-surface temperatures. That produces a plasma that shoots out a nozzle, propelling Vigoride forward.

Unless you need to push the boundaries of what these technologies are capable of, you probably don’t need a highly specialized team of dedicated engineers to build solutions on top of them. If you manage to hire them, they will be bored. If they are bored, they will leave you for Google, Facebook, LinkedIn, Twitter, … – places where their expertise is actually needed. If they are not bored, chances are they are pretty mediocre. Mediocre engineers really excel at building enormously over complicated, awful-to-work-with messes they call “solutions”. Messes tend to necessitate specialization.

Questions about the inclusivity of engineering and computer science departments have been going on for quite some time. Several current “innovations” coming out of these fields, many rooted in facial recognition, are indicative of how scientific racism has long been embedded in apparently neutral attempts to measure people — a “new” spin on age-old notions of phrenology and biological determinism, updated with digital capabilities.

Rational and Irrational Thought: The Thinking That IQ Tests Miss

The participants with relatively strong spatial abilities tended to gravitate towards, and excel in, scientific and technical fields such as the physical sciences, engineering, mathematics, and computer science.