Nov 2023 – Present · 2 yrs 7 mos · New Delhi, Delhi, India · Remote

• 1) System for a stock screener.
• For the development of the stock screener application, I chose a combination of technologies to ensure scalability, performance, and ease of maintenance.
• Backend Technologies:
• Java (Spring Boot): I chose Java with Spring Boot for the backend to leverage Java's strong performance, scalability, and concurrency capabilities, which are ideal for handling large volumes of real-time stock market data.
• MySQL: MySQL was selected as the database for its reliability, strong relational data management features, and ease of integration with Java. MySQL’s ability to handle large datasets and provide fast query performance is crucial for a stock screener handling both real-time and historical stock data.
• Kafka: I integrated Apache Kafka for real-time data streaming and message queuing. Kafka’s distributed architecture provides fault tolerance and scalability, allowing the system to ingest real-time market data feeds from multiple sources without any bottlenecks. It supports the high throughput needed to process large amounts of data while maintaining system resilience.
• Redis: Redis was used as an in-memory cache to store frequently queried data like live stock prices and user-specific preferences.
• Frontend Technologies:
• React: The frontend was developed using React, chosen for its modularity and efficiency in handling dynamic UI updates. React's virtual DOM provides smooth rendering for real-time stock updates, allowing users to view changes in stock prices and other metrics without page reloads.
• WebSockets: I integrated WebSockets to enable real-time communication between the frontend and backend. This allowed for instant updates of stock data, ensuring that users always have access to the most current stock prices.