Yahoo recently published a case study comparing the cost and performance of running Apache Flink and Google Cloud Dataflow for large-scale data pipelines. The study found Dataflow to be around 1.5 to 2 times more cost-effective than self-managed Apache Flink for their tested use cases.
One interesting aspect of the study is how it highlighted the importance of the Dataflow Streaming Engine in driving cost optimization. The Streaming Engine offloads much of the heavy computation to the Dataflow backend, reducing the number of vCPUs required on the Dataflow workers. This results in lower resource utilization and, consequently, lower costs.
Furthermore, the study emphasized the importance of careful configuration and ongoing experimentation when optimizing Dataflow pipelines. The resource-based billing model, in particular, was found to be highly effective in optimizing costs for throughput-based workloads.
Overall, Yahoo's case study provides valuable insights for organizations looking to optimize their large-scale data pipelines. By highlighting the cost-saving benefits of Dataflow, especially when paired with the Streaming Engine and the resource-based billing model, it presents a compelling case for companies to consider Dataflow for their data processing needs.
