Resilient Data Ingestion Patterns in Cloud-Native Data Mesh Architectures

Abstract

In the era of decentralized data ownership and self-serve data infrastructure, cloud-native data mesh architectures are becoming foundational for scalable and agile data platforms. However, ensuring reliable and fault-tolerant data ingestion remains a critical challenge in such distributed systems. This paper explores resilient data ingestion patterns tailored for cloud-native data mesh environments, focusing on patterns that support scalability, observability, recovery, and domain-level autonomy. We examine ingestion strategies under varying failure modes, data velocity, schema evolution, and service disruptions. The proposed patterns—such as event-driven ingestion, CDC (Change Data Capture), and idempotent batch loads—are aligned with cloud-native principles including containerization, orchestration, infrastructure-as-code, and microservices. The paper presents a reference ingestion pipeline leveraging Kubernetes, Kafka, Apache Flink, and cloud-native storage like Amazon S3 or Google Cloud Storage. Through performance evaluation and fault injection tests, we demonstrate how resilient ingestion ensures data availability, consistency, and lineage preservation across data products. Our findings contribute to the design of scalable, fault-tolerant ingestion pipelines that uphold the core tenets of data mesh: federated governance, domain-oriented decentralization, and product thinking for data assets.