Apache Spark is ideal for processing large-scale data in batch jobs, especially in environments where you need to integrate SQL querying, machine learning, and real-time streaming. You would use Apache Spark over Ray in a scenario like analyzing a massive dataset stored in a Hadoop Cluster, where you need to perform complex queries and aggregate computations using Spark SQL, along with machine learning using MLlib.
Ray is particularly beneficial in scenarios where you need to manage complex workflows that require low-latency or real-time processing. For example, if you are building an online gaming application that requires real-time player interactions and state management, Ray's actor model can efficiently handle multiple concurrent tasks and maintain state across them, making it easier to build and scale the application compared to Spark's micro-batching model.