... more about the book our GeekyBookClub chose last time around. I'd have voted against it!
DevTalkLA at last Tuesday's meeting wrapped up the last chapter of Data & Reality, a supposed classic by William Kent. The book proved highly disappointing. It's a kind of skeptical look at data modeling. Skeptics are generally annoying. If data modeling can't be done (objectively), what kind of practical advice does he have to offer us for doing it? Why bother?
This, for example, is from his concluding chapter, entitled "Philosophy":
"So, at bottom, we come to this duality. In an absolute sense, there is no singular objective reality. But we can share a common enough view of it for most of our working purposes, so that reality does appear to be objective and stable." [p. 149]What rot! If there is no objective reality at the base, how the devil can anyone share a "common enough view" of such a non-existent-thing in order for it to magically appear to be a thing, let alone an objective and stable thing?
And if we operate on the (supposedly naive) view that there is an objective reality at the base of what we do, and we do this because we *have to*, then what is the point of removing the naiveté?
Anyway, luckily, this rubbish book is now behind the group. And, also, the side-bar conversations were stimulating as always.
In fact, the great lament of the book club was related to timing. One member noted that the next book, RESTFull Web APIs, which looks extremely promising, was wonderful, but he needed to have read it several months ago, when he encountered these issues!
And TheHackerCIO, also had similar experience, with the Java Performance Book. I came to know about the book through DevTalkLA, and when I got it I realized that the client I had been building a performance engineering environment for would have gotten an order of magnitude better job out of me, if only I had read the book at the time I began the project!
But, such lamentations, trials, and tribulations are part and parcel of the life of the Hacker. We can only know about what we know at the time we know it.