https://github.com/cagandemirmr/flo_sql_server_to_power_bi
In this project, i connect Sql server to Power Bi to visualize my Project
https://github.com/cagandemirmr/flo_sql_server_to_power_bi
data-visualization dataanalysis dataanalyst directquery powerbi queries sqlserver
Last synced: 7 months ago
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In this project, i connect Sql server to Power Bi to visualize my Project
- Host: GitHub
- URL: https://github.com/cagandemirmr/flo_sql_server_to_power_bi
- Owner: cagandemirmr
- Created: 2024-11-11T07:50:36.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-12T07:25:44.000Z (over 1 year ago)
- Last Synced: 2025-03-13T23:12:51.734Z (12 months ago)
- Topics: data-visualization, dataanalysis, dataanalyst, directquery, powerbi, queries, sqlserver
- Homepage:
- Size: 94.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# **FLO_SQL_Server_to_Power_BI**
## **Project Overview**
In this project, I connected an SQL Server database to Power BI to create an interactive dashboard for data visualization. The goal was to visualize real-time data by using Power BI's **Direct Query** feature, ensuring the dashboard always reflects the latest data.
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## **Steps to Build the Dashboard**
### **1. Data Connection**
First, I opened the "Get Data" menu in Power BI and selected "SQL Server". I entered the server details and chose the relevant table. The **Direct Query** option was selected to enable real-time data refresh, ensuring that any changes in the database would be immediately reflected in the Power BI dashboard.
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### **2. Creating Views in SQL Server**
To optimize the data for visualization, I created views directly in SQL Server. This allowed me to summarize data and streamline the process before importing it into Power BI.
Here, I used the **Create View** command to simplify the process:
- Created a view using the `CREATE VIEW` statement to organize data efficiently.
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### **3. Establishing Relationships**
In Power BI, I established relationships between tables to ensure that all imported data is properly linked and can be analyzed cohesively. This step is crucial for accurate visualizations and data integrity.
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### **4. Transferring Data**
Depending on the analysis needs, I created additional tables in SQL Server and imported them into Power BI using the **Direct Query** method. This ensured the data was always up-to-date without the need for manual refreshes.
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## **Dashboard Design**
### **5. Key Performance Indicators (KPIs)**
To highlight the most important metrics, I prepared a series of KPIs:
- **Amount**
- **Total Revenue**
- **Average Recency**
- **Maximum Day Difference Frequency**
- **Average Revenue**
While "Amount" and "Total Revenue" are static values, the rest are interactive, allowing users to drill down into specific data points as needed.
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### **6. Slicers for Filtering**
On the right side of the dashboard, I added slicers to filter the data by years and months. The slicers use a transparent background and corporate colors to match the overall design, providing a smooth and user-friendly experience.
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### **7. Visuals Overview**
- **Tree Map**: Placed on the bottom-right, showing shopping preferences.
- **Donut Chart**: Located in the center, it shows the distribution of orders by channel.
- **Customer Table**: Displays customers with the highest number of purchases.
- **Line and Stacked Column Chart**: In the middle-bottom section, it displays the count of online channels and the sum of online average revenue by channel.
- **Line Chart**: On the right-bottom, a line chart shows changes in offline revenue by year, month, and day.
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### **8. Final Dashboard**
After completing all the visualizations and configurations, the final dashboard was ready, providing a comprehensive overview of key business metrics and trends.
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## **Conclusion**
This project demonstrates how to effectively connect SQL Server to Power BI using the **Direct Query** option and create an interactive dashboard to monitor business performance. With the use of KPIs, charts, and slicers, users can explore data dynamically to gain valuable insights.
### **Technologies Used**
- **SQL Server**
- **Power BI**
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