Running big data workloads on Kubernetes is no longer a niche experiment; it is becoming a standard practice for platform teams. This article examines the three dominant paradigms: offline batch processing, real-time stream processing, and analytical serving. Each pattern has distinct resource requirements, scaling behaviors, and operational challenges. Batch workloads benefit from Kubernetes' ability to manage ephemeral jobs, while streaming workloads demand careful attention to stateful sets and network latency. Analytical serving, often using tools like Presto or ClickHouse, requires predictable performance and efficient data locality. Understanding these trade-offs helps teams choose the right architecture for their use case, avoiding common pitfalls such as over-provisioning or misconfiguring autoscaling. The article also touches on how these patterns can coexist on a single cluster, enabling a unified infrastructure strategy that reduces cost and operational overhead.
Explore the three main patterns for running big data workloads on Kubernetes, including batch processing, real-time streaming, and analytical serving, with practical trade-offs.