![]() Once we determine the tables and fields (you may need to use an extra table for your joins but not for your extracted fields), we can create our extraction, JOINS, and CREATE TABLE queries. That will help us determine not only what our extract and JOIN should look like, but what the CREATE TABLEs for detailed and summary need in terms of fields and datatypes. To start, we need to know what fields we'll need, from which tables. Now that you should have your business question (and an idea of what your answer will be determined by), we're going to start putting our queries together. REMEMBER: Write your drafted queries out on a text file and save them on your PC! Think of something that, using the information in the database, you could run the numbers on to help the business. If you're struggling to understand what a Business Problem is, Chapter 9 of the SQL for Data Analytics book (also linked on the course page in Readings) actually has an example of going through solving one with Postgre (however, you won't be using the shell for the PA). Remember that the transformation function needs to be performed in your detailed table, so that field may not need to be in the summary table. My best advice is to find a problem where using your aggregate function will solve your problem. So I recommend using these three requirements to build your problem. Also keep in mind that you must use 2 tables (1 field at least per table), an aggregate function, and a transformation function. Luckily you should've downloaded the database ERD, so you have an idea of what options you have. You're tasked with creating a "business problem" that the database can solve. This will likely be the hardest part of your project. If you can figure out how to apply the examples to your business problem, you will pass. I've included links to Postgre's official(?) tutorial links for all of these functions.
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