ETL

The ETL (Extract, Transform, Load) visualization project involved managing and representing large volumes of data in a browser-based application. Initially, the project faced significant performance issues due to the browser's inability to efficiently handle the massive datasets. This project aimed to transform raw data from various sources, clean and enrich it, and then visualize the results for data analysis and business intelligence purposes. The objective was to create a responsive and efficient UI that could handle large-scale data visualizations effectively.

Upon joining the project, I identified that the sluggish performance was primarily due to the browser's limitations in managing extensive data directly. To address this, I optimized the user interface by implementing a lazy loading mechanism, which allows the application to load critical data first and defer the loading of additional data until it is needed. This approach significantly improved the application's responsiveness and performance. Additionally, I developed a custom framework tailored to handle large-scale data visualizations efficiently, ensuring a smooth user experience even with substantial data volumes.

The successful optimization of the ETL visualization project required leveraging several advanced technologies and best practices. The custom framework I developed was built using ReactJS & JavaScript for front-end operations, incorporating libraries such as React.js for a responsive user interface. For handling data transformations and processing, tools like Apache Spark and Hadoop were utilized to manage and process large datasets efficiently. The backend was supported by cloud infrastructure, particularly AWS, which provided scalable storage and computing power. This combination of technologies ensured that the application could scale effortlessly and maintain high performance, regardless of the data load.