Real-Time Data Ingestion and Stream Processing for AI Applications in Cloud-Native Environments

Abstract

The fast-growing development of AI uses in areas like predictive analytics, computer vision, and NLP applications demand ultra-low-latency high-throughput data pipelines that can scale. In this paper, the author describes an end-to-end architectural design of fault-tolerant, elastic, and AI-inference responsive real-time data ingestion and stream processing in cloud-native environments. We present the comparative analysis of the state-of-the-art distributed stream processing frameworks, such as Apache Kafka, Flink, Pulsar, and Hazelcast Jet, on the realistic AI workloads. Benchmarks on leading clouds show 99.9% availability, good resource utilization and up to 60% less latency than batch systems. Hybrid edge-cloud architectures are also integrated by us to maximize inference locality and model latency