Serverless architecture in data pipelines, while offering scalability and cost efficiency, introduces several significant risks. One primary concern is vendor lock-in, as pipelines often become deeply integrated with a specific cloud provider's proprietary services, making migration challenging. Performance can be impacted by cold starts, where functions incur initial latency when invoked after a period of inactivity, potentially affecting real-time data processing. Furthermore, observability and debugging become complex due to the distributed nature of numerous ephemeral functions, making it harder to trace data flow and identify bottlenecks. Managing unpredictable costs is another risk, as unexpected invocations or misconfigurations can lead to surprisingly high bills, despite the pay-per-execution model. Additionally, ensuring robust security across many discrete functions and adhering to resource limits for memory and execution time present ongoing management challenges.