Enhanced Vehicle Dynamics 2023: Optimizing Performance and Efficiency for a Sustainable Future

 

Introduction 

In the realm of data analysis for vehicle performance, Power BI proves to be an invaluable tool. Power BI is widely used for storing, visualizing, and gaining insights from data, making it an ideal choice for examining vehicle performance metrics. In this project, we will leverage the capabilities of Power BI to inspect and analyze vehicle performance data efficiently, starting with the creation of an PostgreSQL database for data storage.

 

 

1)Setting up Environment 

2)Database Setup in PostgreSQL

3)ETL Process using Power BI

4)Create visualization

5)Dashboard and Interactivity

6)Testing and validation

7) Deployment

 

 

Setting up Environment :Installing PostgreSQL Database and Power BI

 

Database Creation:Database creation in PostgreSQL

Identify Data Sources: Determine the data tables or views you need to extract data from within your PostgreSQL database.

 

Create Data Connections: Configure Power BI to establish connections to your PostgreSQL database. Provide the necessary credentials and connection details.

 

Extract Data: Utilize Power Query Editor in Power BI to extract data from your PostgreSQL database tables or views.

 

Data Cleaning: Apply data cleaning operations within Power Query Editor to remove duplicates, handle missing values, and ensure data quality.

 

Data Transformation: Transform the data as needed, including filtering, sorting, renaming columns, or applying calculations. Use Power Query's transformation capabilities for this purpose .

 

Data Modelling:To check the type of model, and examining the table has any relationships to other tables.

 

Create charts: Build interactive data charts using Power BI's tools and features, based on the data from your PostgreSQL database.

 

Interactive Dashboard:

Define Dashboard Objectives: Clearly define the objectives and key performance indicators (KPIs) that your interactive dashboard should address using the PostgreSQL data.

 

Add Interactivity: Implement interactive features such as drill-through, filtering, and cross-filtering to allow users to explore the data dynamically.

 

Testing and Validation: Thoroughly test the interactive features and visualizations to ensure they work as expected and provide accurate insights.

 

Deployment: Deploy the interactive dashboard

 

 

By leveraging Power BI for data analysis and PostgreSQL for data storage, we will achieve a comprehensive understanding of the vehicle performance dataset. This process will enable us to identify trends and valuable insights related to vehicle performance. The final output will be a Power BI dashboard that provides an interactive and insightful representation of the vehicle performance data, aiding in data-driven decision-making and deeper exploration of vehicle dynamics.

GitHub: For All files