| File Name: | Learn Procedural SQL by Building a Chess Game in PostgreSQL |
| Content Source: | https://www.udemy.com/course/learn-procedural-sql-by-building-a-chess-game-in-postgresql |
| Genre / Category: | Other Tutorials |
| File Size : | 1.7 GB |
| Publisher: | Konstantinos Malonas |
| Updated and Published: | July 24, 2025 |
In this hands-on course, you’ll master PL/pgSQL—the procedural language of PostgreSQL—by building a complete chess game from the ground up. From basic move validation to advanced game mechanics like resurrection and RUA (Check) detection, you’ll learn how to turn SQL into a powerful backend logic engine.
This is not just another tutorial about syntax. Instead, you’ll go deep into how procedural SQL works by applying it in a real-world scenario. You’ll also learn how to integrate Python with PostgreSQL to control gameplay, handle moves, and manage game flow in a clean and interactive way.
What you’ll build:
- A chess board using PostgreSQL arrays
- Custom piece movement functions (pawn, bishop, rook, knight, queen, king)
- Validation logic for legal moves
- Enemy king detection and RUA (check) logic
- Resurrection mechanics using stored procedures
- A fully playable game loop in Python with dynamic prompts
Technologies and Tools used:
- PostgreSQL (PL/pgSQL)
- Python
- DBeaver
- VSCode
By the end of the course, you’ll not only be proficient in PL/pgSQL but also able to apply procedural programming logic to any database-driven application. Whether you’re a developer, data engineer, or database enthusiast—this course will sharpen your backend logic skills through a fun and intellectually rewarding project.
Who this course is for:
- Beginner to intermediate programmers who want to learn PostgreSQL procedural SQL by building a real chess game with Python integration.
DOWNLOAD LINK: Learn Procedural SQL by Building a Chess Game in PostgreSQL
Learn_Procedural_SQL_by_Building_a_Chess_Game_in_PostgreSQL.part1.rar – 1000.0 MB
Learn_Procedural_SQL_by_Building_a_Chess_Game_in_PostgreSQL.part2.rar – 736.7 MB
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