Data:

1. The combined aggregate of electronic flotsam and jetsom accumulated over many years that must now be correctly interpreted, properly cataloged, expertly formatted, sorted, sifted, filtered, and perfectly presented in order to provide executives and managers cover when forced to make decisions.
2. Data is the plural of datum. Datum means point in the Greek. However, Data are usually pointless.

Data Model:

1. Emaciated and starved structure on which the skimpiest of data are hung as demonstration before the whole picture is modified, enhanced, and all blemish and flaws are removed for executive presentation to an inattentive and disinterested audience.
2. A fractional-scale structure, built with different materials, on a different platform, carrying no cargo, to be used to demonstrate the viability of something that has never existed, and will have greatly different characteristics and challenges once it is built to scale.
3. Context-sensitive palette on which domain experts derive paint-by-number masterpieces of data-impressionism and schema-cubism where, as Justice Potter Stewart would have noted of the construct, “I’ll know it when I see it.”

Data Mining:

1. The discoveries of the meaning of life, the universe, and everything after abandoning the tried-and-true methods of interpreting the entrails of chickens and deciding that the same, or messier, source could be found in a database.
2. Realizing that risking a few thousand miners would be a small price to pay for superhuman insight, so long as you’re not the miner.
3. Realizing that, with enough strange-sounding terms, enough technical people with bad social skills, and enough support from the technical journals, the 20 standard answers in a Magic 8-Ball could be repackaged and sold for millions.

Sixteen Terabytes and whattya get?
Another run older and deeper in technical debt.
Tell Saint Hadoop that I can’t go;
I own my analytics to the Google Store.

Data Science

1. Renaming reporting to something more impressive.
2. Using the word Data as a negating predicate for the noun, science, in the same way Social is used in the term Social Science.[**]
3. A science in the same sense that bleeding patients was called medicine.

Professor Inksmatter, working diligently in his lab, distilled fifteen terabytes of corporate data into two kilograms of manure, six shots of pure tequila, and five liters of bean juice. This supported his theorum that you can run a company on data alone, but not for long.

[**] Using “social” as a predicate negates the next word: social science, social justice, social security, social media…

Dataset:

A group of things which, by collection, have been conferred with a mantle of respectability.

Data Type:

1. A Procrustean Bed where information must fit a structure that limits how much information will fit.
2. A rigorous specification for physical storage and semantics documented in such a way that even a programmer can understand engineering.

Data Warehouse:

1. A very large accumulation of Data. While warehouses have inventory, managers, and known transportation methods, Data Warehouses usually don’t. This makes them Data dumps.
2. Large accumulations of data, available only in large quantities at huge discounts.
3. Data with low overhead and high volume.

Debugging:

Unplanned feature removal.

Delegate:

1. A stand-in for a method, with limits; as those found when sending a delegate for the groom.
2. For C++ programmers who have not yet the skill for self-destruction, a pointer to a function without the fun.
3. The semantic definition of a Delegate; in this way the thing casting the shadow is never more real than the shadow itself.

Deprecate:

To disavow without dissolution.

Design:

Overlaying logic as an afterthought atop production.

Developer:

1. In photography, the chemical solution that reduces latent images on negatives to visible layers.
2. In real estate, the visionary who sees homes and commerce where currently only livestock graze.
3. In applications development, the insignificant and unstable layer of corporate liability burdened blessed with the obligation opportunity to reveal the unseen and create the non-existent.

DevOps:

1. The corporate acknowledgement that, while leadership may be preferable, compromise is easier to sell.
2. An agreement by the non-technical corporate leaders to weaken all technology in the company by aggregating them into a new group with the demand that they now compromise all principle in pursuit of objectives each detest for separate reasons.
3. That new thing that will be just as much a failure as Agile, and for exactly the same reasons.
4. Familiar-sounding linguistic maw, capable of consuming all logic, increasing all expectations, with the alimentary result worse than any pessimist could have warned, all in the name of promising another new suit of technical clothes.
5. The management presumption that fundamental disagreements in technology and direction are the result of mistakes in the organizational chart, rather than any deep-seated disparity in professional principles; demanding that incompatible substances be forced into close proximity in the hope that placing caustic irritants as close as possible to each other will save money and boost good will.
6. The latest desperate attempt to contain costs, increase submissiveness of developers and admins, define the most complex technical issues with meaningless buzzwords, and hopefully save the failing project and the concomitant failing manager.
7. The mythological reversal of the popular Hydra whereby the beast has many bodies but only one head.

When DevOps dances, who leads?

Document Object Model:

1. The Sykes-Picot equivalent for the browser which set boundaries and borders of influence in the web page for the purposes of reducing a monolithic power to bickering fiefdoms.
2. The establishment of cross-platform — and therefore, competing and dissenting — implementation(s) of standards for parsing a web page into a labyrinth of special characters and notations where once static and forgettable content came to retire.
3. Peace in our time between JavaScript and JScript, and between Netscape Navigator and Internet Explorer.
4. Technical equivalent of a social structure; instituted to prevent lesser members of one class from harrying their betters in another class.

Documentation:

Your notes.

Domain:

1. A theoretical island of logic and process where all things remain equal.
2. A set of data tables and columns that store unambiguous information related to only one such theoretical island.
3. The smallest and simplest contiguous system that still retains all the state and properties of the system; like an atom; or in complex systems, the bomb that bears its name.
4. A physical or logical topography where the owner holds complete sovereignty.
5. The manager’s inaccurate presuppositions regarding the way things work; usually called his “field of expertise.”

Domain Violation:

1. An unpleasant stroll outside the boundaries of your universe to a place lacking recognizable landmarks: where hares are late and only the cat’s smile exists.
2. Walking beyond the edge of the Grand Canyon because you saw Wile E. Coyote do it on The Roadrunner Show.
3. Confusing the bull for the cow and learning that it doesn’t matter if you brought your own stool and pail.
4. Allowing other people’s problems to become requirements in your design.
5. An attempt to derive semantics from syntax.
6. The first step on the long road to perdition.

Durable:

Over-engineered with exception-handling that will never be used.

Dynamic:

1. The state of any of the myriad possible application project properties not constrained from change by severe penalty.
2. The state of unstated needs and unexpressed requirements.
3. The uncontrollable led by the unknowable.
4. Code interpreted at runtime, providing the same protection from embarrassment as putting children on live TV.

When asked to describe the world’s weather, Noah responded, “dynamic.”

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