This project involves performing financial and supply chain analytics on Atliq hardwares data. The primary objective is to answer various analytical questions related to sales, gross sales, market, product, customers, and supply chain forecasting.
In this endeavor, I took a closer look at Atliq Hardware, a fictional leader in computer hardware. The challenge was to empower the management with valuable insights using MySQL.
- Forecast value: The predicted value of demand for products.
- Net error: The difference between forecast value and actual value.
- Absolute Net error: the positive value of net error even if the net error is negative
- Absolute Net error percent: Total absolute net error divided by total forecast value
- Forecast accuracy percent: 100 – absolute net error percent
- DDL and DML operations
- Implementing Data Integrity in Entity-Relationship Diagrams
- Handling various data types: numeric, string, date/time, JSON, and Spatial
- Using Stored Procedures and User-defined functions
- Performance optimization by reducing the overall query execution time
- Using CTEs (Common Table Expressions), Temporary tables, and Database Views.
- Windows functions (OVER, PARTITION BY clause, ROW_NUMBER, RANK, DENSE_RANK).
- Using Triggers, Indexes
- Grasping the significance of Primary and Foreign Keys