Adaptive Data Pipelines: A Cloud-Native Approach to Real-Time Analytics and Fault Tolerance

The increasing prevalence of real-time data streams
across many different areas of industry has accelerated the
need for adaptive, fault-tolerant data pipelines, capable of
consuming dynamic workloads and processing them on cloudnative
systems. The default solution of using static scaling
typically does not meet the low-latency and reliability needs of
data pipelines in ad-hoc and unpredictable environments. This
paper presents a framework for a new cloud-native adaptive
data pipeline that integrates advanced stream processing
engines with autonomous artificial intelligence-powered control
loops to perform resource orchestration with fault-tolerance.
The architecture uses Apache Flink, Kubernetes orchestrators,
and reinforcement learning-based controllers to perform realtime
scaling, low-latency analytics, and resilient recovery from
unforeseen failures through checkpointing and chaos testing.
Experimental evaluation using publicly available dataset
benchmarks demonstrates a notable increase in throughput
and a decrease in latency and recovery time compared with
existing state-of-the-art systems. The outcome is to demonstrate
the capacity for intelligent, self-optimizing pipelines to support
critical real-time applications as the industry transitions to
data-driven applications in cloud-native environments.
Keywords— Adaptive Data Pipelines, Cloud-Native
Architecture, Real-Time Analytics, Fault Tolerance,
Reinforcement Learning, Stream Processing.