This project focuses on designing and implementing a data analytics pipeline using Apache Spark for large-scale data processing and Power BI for interactive data visualization. The goal is to demonstrate how modern big data tools can be used to transform raw data into meaningful insights that support data-driven decision-making.
The project begins with data acquisition and preparation, where raw data from open datasets is loaded into Apache Spark and cleaned through preprocessing steps such as handling missing values, correcting data types, and filtering invalid records. This ensures data quality and reliability for further analysis.
Next, analytical operations are performed in Spark using both the DataFrame API and Spark SQL. These operations include filtering, grouping, aggregation, and computation of descriptive statistics. Optimization techniques such as caching and broadcast joins are applied to improve processing performance and efficiency.
After processing, aggregated datasets are exported and visualized in Power BI. Interactive dashboards are created using charts, maps, and slicers to highlight key trends, patterns, and relationships within the data. These visualizations make complex data easier to understand and explore.
Overall, the project demonstrates an end-to-end analytics workflow from raw data processing to insight generation using scalable big data technologies. It highlights the importance of efficient data engineering, analytical thinking, and clear visualization in extracting value from data.