Polars is often preferred for smaller to medium-sized datasets that fit in memory and require fast processing since it's highly optimized for speed and efficiency. You would use Polars when you need quick exploratory data analysis on your laptop or local machine, especially if working with DataFrame operations that benefit from parallel execution. For example, if you have a dataset of customer transactions with a few million rows, and you need to perform data cleaning and aggregations quickly, Polars would typically be more responsive and faster than Spark.
Apache Spark is ideal when you need to process large datasets across a cluster of machines, especially for complex tasks involving data streaming, machine learning, or integration with big data ecosystems. For example, if your organization has petabytes of data stored in Hadoop and needs real-time analytics for business intelligence, Spark would be a better choice due to its ability to handle distributed processing efficiently.