How to Answer Handling Real-Time Data at X (Twitter)

To design a system for efficiently handling real-time tweet streams, I would first identify key requirements such as high throughput and low latency to ensure timely updates for trends and notifications. I would consider an event-driven architecture utilizing tools like Apache Kafka for message queuing and stream processing. This setup would allow for efficient handling of large volumes of tweets, enabling real-time analytics and notifications. For data storage, I’d use NoSQL databases like Cassandra that provide high write and read performance, ensuring scalability as user engagement increases. To ensure resilience, I’d implement partitioning and replication across distributed systems, enabling fault tolerance and minimal downtime during peak loads. This architecture can seamlessly adapt as Twitter scales, maintaining the user experience in real-time interactions.