Apache Spark is particularly useful when you need to process large datasets across a distributed cluster and require advanced functionalities such as real-time data processing and machine learning. A specific scenario would be processing terabytes of streaming data from IoT devices in real-time, where Spark Streaming can handle the data efficiently compared to Dask.
Dask can be advantageous when you have a smaller dataset that fits on a single machine but you want to use parallel processing to speed up calculations. For example, if you are using large Pandas DataFrames for data manipulation and need to scale out to multiple cores on your laptop, Dask can help you parallelize that workload easily. Another scenario could be when you want to quickly prototype a solution on a local machine before scaling up to a larger cluster, where you can leverage Dask's simplicity in a familiar Pandas-like interface.