Rukshan Edirisinghe

Portfolio detail

Project Description:

Our objective is to enhance our internet sales reporting by transitioning from static reports to dynamic visual dashboards. This project focuses on gaining insights into our sales performance, including product sales, customer interactions, and sales trends over time. Additionally, we aim to provide filtering options to view sales data by salesperson, products, and customers. We regularly compare our sales figures against a budget, which is for the year 2023. Typically, our analysis considers data from the past two years.

SQL Queries for Data Cleansing:

Cleansed FACT_InternetSales Table:

This query extracts data from the FACT_InternetSales table. It includes essential fields such as ProductKey, OrderDateKey, DueDateKey, ShipDateKey, CustomerKey, and SalesAmount. Other fields have been commented out to focus on the key information needed for analysis. The WHERE clause ensures that only data from the last two years is included in the extraction.

Cleansed DIM_Products Table:

In this query, data is extracted from the DIM_Products table. It includes product-related information such as ProductKey, ProductItemCode, Product Name, Sub Category, Product Category, Product Color, Product Size, Product Line, Product Model Name, and Product Description. Other fields have been omitted to streamline the dataset.

Cleansed DIM_Customers Table:

This query focuses on the DIM_Customers table and extracts customer-related data. It includes CustomerKey, First Name, Last Name, Full Name, Gender, DateFirstPurchase, and Customer City. Other fields are excluded to keep the dataset concise.

Cleansed DIM_Date Table:

Data is extracted from the DIM_Date table with a focus on date-related information. It includes DateKey, Date, Day, Month, MonthShort, MonthNo, Quarter, and Year. The WHERE clause ensures that data from the year 2021 and beyond is included in the dataset.