The challenges of processing streaming data, as seen through the example of a fictitious bank's credit card transactions, arise from the need to manage large volumes of event data in real-time while ensuring accuracy and security. To address these challenges, the bank adopts an event streaming platform like Apache Kafka to queue event data, but also realizes that querying the transactional event data as it flows into the database could help identify suspicious transactions. From a developer's perspective, building applications with streaming data requires consideration of serialization formats, schemas, late-arriving data, operational complexity, and security. Stream processing can help address these challenges and enable real-time use cases such as fraud detection, hyper-personalization, and predictive maintenance.