SQL Data Extraction Project

This project focuses on extracting valuable insights from the Adventure Works database using SQL queries.The extracted data is further processed, visualized, and presented using Power BI.

Key Features

Data Extraction: Utilizing SQL queries to extract specific information from the Adventure Works database.

Coding Skills: Implementation of sub-queries, concatenation, joins, mathematical operations, ranking, and dense ranking.

Export to Excel: Exporting SQL query results to Excel for further analysis.

Power BI Visualization: Creating dynamic dashboards with Power BI to present insights effectively.

Project Steps

Google Sheet Data: Link to Google Sheet - Contains data for all seven questions.

Power BI Dashboard: Link to Power BI Dashboard (PDF Format) - Visual representation of extracted insights.

Relational Database Wiring: Link Wiring Diagram - Illustrates the connections between tables.

Steps to Replicate

Data Extraction:Execute SQL queries provided in the code file to extract information.

Export to Excel:Save the SQL code file and export results to Excel.

Power BI Visualization: Import the Excel file into Power BI. Apply data transformation, create visualizations, and design dashboards.

Presenting Insights: Leverage Power BI features such as tooltips, color combinations, and dynamic information for effective presentation.

Skills Demonstrated

SQL Coding Proficiency

Data Mining and Extraction

Excel and Power BI Skills

Data Visualization Techniques

Feel free to explore the provided links to understand the project data, visualizations, and database relationships

Adventure Works SQL: Extracting 7 Important Insights

1. What are the top 10 highest selling products in the database?

   • Use the salesorderdetail table as the base table, with LineTotal as Sales. Create a Pie chart to depict this information.

2. Who are the top 10 highest spending customers in the data along with their address and address type information?

   • Use the salesorderheader table as the base table, with TotalDue as sales. Create a Bar chart to depict this information.

3. Calculate the Sales by Sales Reason Name and Reason Type. Also find the best and worst performing Sales Reason in terms of Sales.

   • Use the salesorderheader table as the base table, with TotalDue as sales. Create a Bar chart to depict this information.

4. Calculate the average number of orders shipped by different Ship methods for each month and year.

   • Use the salesorderheader table as the base table, with TotalDue as sales. Create a Line chart to depict this information.

5. Calculate the count of orders, maximum and minimum shipped by different Credit Card Type for each month and year.

   • Create a chart of your choice to depict this information.

6. Which are the top 3 highest selling Sales Person by Territory for each month and year?

   • Create a chart of your choice to depict this information.

7. Calculate the count of employees and average tenure per department name and department group name.

   • Create a table to depict this information.