What is your experience with near real‑time projects?

I have led several near real‑time projects that process high‑velocity data streams with sub‑second latency. In one project, we built a fraud‑detection pipeline using Apache Kafka for ingestion, Apache Flink for stateful stream processing, and Redis for low‑latency lookups. We defined tumbling windows of 1 second and used event‑time timestamps to ensure accurate ordering. The pipeline achieved an end‑to‑end latency of 150 ms, meeting the SLA. Another project involved real‑time inventory updates for an e‑commerce platform. We used AWS Kinesis Data Streams to capture order events, processed them with AWS Lambda, and updated a DynamoDB table with a global secondary index for quick queries. We implemented a watermarking strategy to handle out‑of‑order events and used DynamoDB Streams to trigger downstream services. These experiences taught me the importance of precise time‑stamping, efficient state management, and robust monitoring to maintain consistency and performance in near real‑time systems.