0In this lesson, we will explore a real-world data analytics use case where data is scattered across Excel files, CSVs, and a SQL database. We’ll demonstrate how to use Advanced Excel, Power Query, SQL, Tableau, and Power BI to create a seamless data solution—from extraction and transformation to visualization and actionable insights.
retail company wants to analyze its sales performance. The data is stored across multiple formats:
- Excel Files: Monthly sales data by region.
- CSV Files: Product master data.
- SQL Database: Customer purchase history.
The goal is to create an interactive dashboard that provides insights into:
- Regional sales performance.
- Top-performing products.
- Customer purchasing behavior.
Step 1: Data Extraction
- Advanced Excel: Quickly review and clean the Excel and CSV files using formulas, filters, and conditional formatting.
- SQL: Query the database to extract relevant customer purchase data using SELECT statements and JOINS.
Step 2: Data Transformation
- Power Query: Combine and transform the Excel and CSV files into a unified format. Merge this data with the SQL output, removing duplicates and handling missing values.
Step 3: Data Modeling
- SQL: Use database tools to optimize data structure, applying relationships and indexing for efficient querying.
- Power Query/Power BI: Create relationships between datasets and build calculated columns where necessary.
Step 4: Data Visualization
- Tableau: Design an interactive dashboard showing sales trends, regional comparisons, and top products. Use advanced visualization techniques like heat maps and bullet charts.
- Power BI: Create dynamic dashboards with slicers and drill-through capabilities for deep insights into customer behavior and sales metrics.
