Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters of cloud resources. Yet, selecting appropriate computational resources for dataflow jobs is often challenging. For efficient execution, individual resource allocations, such as memory and CPU cores, must meet the specific resource demands of the job. Meanwhile, the choices of cloud configurations are often plentiful, especially in public clouds, and the current cost of the available resource options can fluctuate. Addressing this challenge, we present Flora, a low-overhead approach to cost-optimizing cloud cluster configurations for big data processing. Flora lets users categorize jobs according to their data access patterns and derives suitable cluster resource configurations from executions of test jobs of the same category, considering current resource costs. In our evaluation on a new dataset comprising 180 Spark job executions on Google Cloud, Flora's cluster resource selections exhibit an average deviation below 6% from the most cost-optimal solution, with a maximum deviation below 24%.