Design a Service for Real-time Analytics

To design a real-time analytics service for high-volume clicks, I would start by defining non-functional requirements: ingesting millions of events per second with sub-second query latency for dashboards. For the ingestion layer, I'd use Amazon Kinesis Data Streams to buffer incoming data, ensuring durability and ordering. Regarding architecture, I recommend a Kappa approach where all data is treated as a stream. This simplifies operations by removing the need to maintain separate batch and speed layers, which aligns well with AWS managed services like Kinesis Data Analytics. We would process events directly through Flink or Spark Streaming to compute rolling aggregates, storing results in DynamoDB for low-latency reads and S3 for long-term archival. If we needed complex historical reprocessing that streaming alone couldn't handle efficiently, a Lambda architecture might be better, but it introduces significant operational complexity in maintaining two code paths. Given Amazon's focus on operational excellence, Kappa reduces the risk of data drift between batch and real-time views. To ensure reliability, I'd implement exactly-once processing semantics using checkpoints. Finally, the query layer would expose an API Gateway backed by the DynamoDB store, allowing analysts to fetch live metrics instantly. This design balances high throughput with the low-latency requirements essential for real-time business intelligence.