DATA SCIENCE USING JDBC AND SQL SERVER WITH OBJECT-ORIENTED APPROACH AND APACHE NETBEANS IDE

Book excerpt: This book is SQL SERVER version of our previous book titled “DATA SCIENCE USING JDBC AND MYSQL WITH OBJECT-ORIENTED APPROACH AND APACHE NETBEANS IDE”. This book uses the SQL SERVER version of Sakila sample database which is a fictitious database designed to represent a DVD rental store. The tables of the database include film, film_category, actor, customer, rental, payment and inventory among others. The Sakila sample database is intended to provide a standard schema that can be used for examples in books, tutorials, articles, samples, and so forth. You can download the sample database from https://viviansiahaan.blogspot.com/2023/05/data-science-using-jdbc-and-sql-server.html. In this project, you will design the form for every table and you will plot: top 10 film distribution by release year; top 10 film distribution by rating; top 10 film distribution by rental duration; top 10 film distribution by language; film distribution by categorized rental rate; film distribution by categorized length; film distribution by categorized replacement cost; top 10 film distribution by actor name; top 10 actor name distribution by average rental rate; top 10 actor name distribution by average replacement cost; film distribution by rating; rating distribution by average rental rate; rating distribution by average replacement cost; top 10 film distribution by category name, category distribution by average replacement cost; category distribution by average rental rate; category distribution by length; top 10 city distribution by by country; top 10 address distribution by district, top 10 address distribution by country; top 10 address distribution by city; top 10 address distribution by district; top 10 address distribution by country; top 10 address distribution by city; top 10 inventory distribution by release year; top 10 inventory distribution by film rating; top 10 inventory distribution by film language; top 10 inventory distribution by film rental duration; top 10 inventory distribution by city; top 10 inventory distribution by country; top 10 customer distribution by country; top 10 customer distribution by city; top 10 customer distribution by district; top 10 customer distribution by store country; top 10 customer distribution by store city; top 10 customer distribution by store district; top 10 staff distribution by country; top 10 staff distribution by city; rental distribution by year of rental date; rental distribution by month of rental date; 10 rental distribution by week of rental date; rental distribution by day of rental date; rental distribution by quarter of rental date; rental distribution by film release year; rental distribution by film duration; rental distribution by film rating; top 10 rental distribution by staff name; rental distribution by film language; top 10 rental distribution by film title; rental distribution by customer active; top 10 rental distribution by film category; top 10 rental distribution by actor name; top 10 rental distribution by customer name; top 10 rental distribution by customer city; top 10 rental distribution by customer country, top 10 rental distribution by customer district; payment distribution by year of payment date; payment distribution by month of payment date; top 10 payment distribution by week of payment date; payment distribution by day of payment date; payment distribution by quarter of payment date; payment distribution by film release year; payment distribution by film duration; payment distribution by film rating; top 10 payment distribution by staff name; payment distribution by film language; top 10 payment distribution by film title; payment distribution by customer active; top 10 payment distribution by film category; top 10 payment distribution by actor name; top 10 payment distribution by customer name; top 10 payment distribution by customer city; top 10 payment distribution by customer country; and top 10 payment distribution by customer district.