“AQE” in Spark stands for Approximate Query Engine. It is a feature in Spark that allows users to perform approximate queries on large datasets with high efficiency, while also providing a configurable level of accuracy. The Approximate Query Engine uses statistical techniques to estimate the results of a query, rather than calculating them exactly, which can significantly reduce the amount of computation required.

The AQE feature is particularly useful for interactive workloads, where users need quick responses to ad-hoc queries, and for applications that require frequent queries on large datasets. AQE can be used with various types of Spark workloads, including Structured Query Language (SQL), DataFrames, and Datasets.

AQE is designed to work seamlessly with Spark’s Catalyst optimizer, which provides a unified query optimization framework for Spark SQL. AQE can also leverage various other Spark optimizations, such as partition pruning and data skipping, to further improve the efficiency of queries.