Use Apache Spark when you need high-performance data processing capabilities, particularly for complex data transformation and iterative algorithms. A specific scenario would be when building a machine learning model predicting customer behavior on large datasets, where Spark's in-memory processing can significantly reduce the time taken to iterate over training datasets.
Google Dataflow is ideal when you need a serverless, easy-to-use solution for processing both batch and streaming data, particularly if you are already using other Google Cloud services. One specific scenario could be processing real-time data from a sensor network to monitor and analyze data continuously without needing to manage the underlying infrastructure.