Unit 4 Written Assignment Anonymous Department of Computer Science, University of The People MSIT 5210: Databases Giri Anandhi February 29, 2024.
Author Note: My paper is written and cited in APA7 format via the guidelines from Purdue OWL Online Writing Lab. My name is listed as "Anonymous" on the cover page since our Written Assignments are meant to be a double-blind system. Thank you for assessing my work. For Your Reference: My target organization is Amazon. ________________________________________
1. What is the difference between a. and a. Data Manipulation (DML) Scripts for Creating Tables: As I continue to evolve the database development plan for Amazon, I turn my attention to the practical application of Structured Query Language.…show more content… Creating the Products table CREATE TABLE Products ( ProductID INT PRIMARY KEY, Name VARCHAR(255), CategoryID INT, Price DECIMAL(10,2), Description TEXT, FOREIGN KEY (CategoryID) REFERENCES Categories(CategoryID) );.
Create the Categories table CREATE TABLE Categories ( CategoryID INT PRIMARY KEY, Name VARCHAR(255), Description TEXT ); and select the…show more content… What is the difference between a'smart' and a'smart'? Narrative In crafting these SQL scripts, I made sure that every line of code I wrote and every table I designed was meticulously aligned with Amazon's overarching goal of being the world's most customer-centric company. My scripts for creating tables ensure that the database structure is sound, scalable, and ready to handle the vast amounts of data that flow through Amazon's systems daily. The data manipulation scripts reflect the dynamic nature of Amazon's business, allowing for the database to be updated in real-time as transactions occur, customers interact, and inventory changes. The reporting queries I created are not just statements; they are tools for insight. They empower Amazon to understand their customer behaviors, product performance, and overall sales trends, which are crucial for making strategic decisions that enhance customer satisfaction and operational efficiency. Through this SQL-driven approach, I am enabling Amazon to maintain the integrity and reliability of their data, which underpins their commitment to providing exceptional service. My focus on aggregate functions is particularly significant for Amazon's decision-making process. By utilizing AVG, MAX, MIN, COUNT, and SUM, I am providing a pathway for Amazon to analyze their vast datasets to identify trends, forecast demand, and optimize their supply chain (Monge, n.d.). The insertion scripts are a testament to the importance of data richness
