Apache Spark is ideal for scenarios where you need to process large volumes of data across a distributed cluster. For example, if you're running a large-scale data processing job that involves streaming data from multiple sources (like Kafka) and performing real-time analytics, Spark's ability to handle distributed computation will greatly enhance performance. In contrast, DuckDB is more suited for single-node, ad-hoc analytics on smaller datasets.
DuckDB is ideal for single-node, in-memory analytical workloads, especially when working with smaller datasets or during exploratory data analysis. Use it when you need fast querying without the overhead of managing a distributed system. For instance, if you're a data scientist analyzing a local CSV file, DuckDB can provide quick and efficient insights without the complexity of setting up Spark.