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Venkata Vara Prasad Jaladanki • Peerlist

🚀 Unveiling the Architecture of Leading Tech Giants: UML Diagrams for Ola, Uber, and Zomato 🚀

I'm excited to share a project that I've been working on to dissect and understand the architectural design of some of the world's most innovative companies - Ola, Uber, and Zomato. 📊💡

These UML (Unified Modeling Language) diagrams offer a visual representation of the core systems that drive these tech giants, highlighting how they manage to deliver seamless experiences to millions of users every day. Here's what you'll find in each diagram:

🔹 Ola: Explore the intricate interactions between users, drivers, ride management, payment systems, and customer support that enable Ola to operate efficiently across multiple cities.

🔹 Uber: Delve into the robust architecture that supports Uber’s global operations, featuring components like user management, ride matching algorithms, payment processing, and real-time tracking.

🔹 Zomato: Understand how Zomato connects diners with restaurants through a sophisticated network of user interfaces, restaurant management, order processing, and delivery tracking systems.


Visualizing Complex Systems: Breaking down sophisticated systems into manageable and understandable components.
Facilitating Communication: Providing a common language for developers, stakeholders, and engineers to discuss and improve system designs.
Ensuring Robustness: Identifying potential issues in system architecture early in the design phase.

Next Steps:
I believe that sharing these diagrams can spark valuable discussions about system design, architecture, and best practices in software engineering. I would love to hear your thoughts, feedback, and any experiences you might have had working with similar systems.

System Design for Uber, Zomato, Zerodha.

A highly scalable and efficient bulk action platform capable of performing various bulk
actions on CRM entities. It should be flexible and adding new bulk actions on it should be easy.
The system should be able to handle large volumes of data (up to a million entities) with high
performance, extendability, and robust error handling.