Here’s a real-world story: Data collection and analysis is, ironically, for a data management profession, one of the toughest parts of my job. I regularly pull data from our various databases and packaged application reporting systems for CRM, licensing, and user information.
If you’ve ever worked with a packaged product, such as a CRM system, that includes reporting and querying features, then you know how inflexible they are. It can be infuriatingly difficult to get data out of these systems, systems for which you paid big money, without losing sleep, hair, and peace of mind. Working with our own custom licensing and user information databases is no big deal, of course, since it’s built on a standard relational database and uses SQL. So resorting to regular SQL queries, while time consuming, is effective, convenient, and reusable. At least, it should be no big deal. But then issues start to arise. Non-standard data types might be in use. Some tables are denormalized and contain too many bits of distinct data within a single value. Problems, and frustrations, start to rise.
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