Albertina Elsa May 2, 2021 Spreadsheet
He was an ex-divorce attorney who had seen firsthand what a messy thing divorce was when lawyers were involved. He developed a program where a couple would meet together, with him present, and work through the divorce piece by piece. Property, finances, kids, pensions. It was a great system. And he was cheap! Relatively speaking. It took about 10 months including some stops and starts (”what do you mean you want some of my inheritance? if you want my inheritance then you STAY married to me”) but in the end we were able to come to terms with each other in a reasonable and fair way.
He grossed $2,000 a week for his bosses, and earned slightly less than $500 for himself. Still, the wages kept him in seeds, bowling shoes, stick pins, and a Platinum Buddy Holly Fan Club Membership. Lester‘s favorite word was ”crapola,” and he applied it to the ball bearing factory‘s antiquated data processing system in coats as thick as the olive drab membrane clinging to the smudgy glass before him. ”You piteous piece of crapola!” he‘d hiss at the computer when error messages flashed across its screen or its ancient system locked under the demand of crunching numbers to the tenth decimal point. ”Some day I‘ll throw your sorry ass into one of those melting pots out there!”
The former hangs like a transparent curtain four feet above the floor and shrink-wraps itself to anyone bold enough to attempt passing through. The latter represents a fragrant blend of beer, cheap cologne, and unkempt toilets, and assaults an unsuspecting visitor‘s nose like an aggressive index finger. By Saturday, the fragrance would be pungent enough to cause mere mortals to speak in tongues. Lance led the way with Lester in tow, dodging around dark figures that emerged from the nicotine and odoriferous fog. Lester had difficulty keeping up, licking the lenses of his glasses and tie-drying them as they wove their ways toward diffused light they assumed was the bar area where lusty women awaited.
Spreadsheets such as Microsoft Excel are well suited to tasks involving the manipulation of small amounts of related data. Working out a budget, producing visual reports, organizing lists and calculations that involve many variables are all tasks well suited to a spreadsheet. There are some data related tasks however that spreadsheets such as Microsoft Excel are not suited for. Tasks involving the processing and combination of large sets of data for example are generally not well suited to spreadsheets. There is another technology with a long history and theoretical background that specializes in these sorts of tasks. That technology is relational databases. The most common way people insert data into and extract data from relational databases is via the language of Structured Query Language.
Lester loved his numeric universe, but this was not how he had envisioned his life unfolding; flying hither and thither from his hometown of Hershey to wherever his firm wished to send him. Just because he was 38, single, and still living with his folks didn‘t mean his employer should take advantage of him which, in fact, his company did on a regular basis. After all, Lester had other important interests, too. The ”Four Bs” he called them: Botany, bowling, bugs and Buddy Holly. Myriad plants crowded his tiny room in his parent‘s house, forcibly sucking carbon dioxide out of anyone who entered. Bowling trophies – ranging in size from tiny silver cups to massive bronze edifices shaped like the Empire State Building – claimed space not dominated by flower pots, planter boxes, and hanging baskets.
These common complaints with Microsoft Excel filtering are heard time and time again by engineers, accountants, management consultants, bankers and finance professionals who work with data in Excel spreadsheets on a daily basis. Many spreadsheet users including financial modellers (who seem to be leading the charge) are turning towards Excel Add-ins and software tools that plug into Microsoft Excel to help them improve the in-built filtering logic of Microsoft Excel and thus analyse certain data sets quickly and easily.
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