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.
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.