[Flink SQL] is a powerful data processing engine that allows developers to process and analyze large volumes of data in real time using ANSI standard compliant SQL syntax. It provides users with a declarative way to express data transformations and analytics on streams of data, supporting various SQL operations such as filtering, aggregating, joining, and windowing. Flink SQL is highly interoperable with other Flink APIs, allowing developers to use one or many APIs depending on their requirements. Its unified API for batch and stream processing enables users to apply features to both bounded and unbounded data, making it well-suited for complex data processing tasks. With its advanced optimization techniques, Flink SQL ensures efficient query execution and minimal resource usage, even in complex scenarios. It offers various joins, aggregations, and windowing capabilities, including temporal joins, lateral joins, tumbling windows, hopping windows, and cumulative windows. Flink SQL's streaming mode has optimizations for temporal joins that take advantage of the time-based nature of the data, making them more efficient than regular joins. When choosing between streaming and batch processing modes in Flink SQL, consider the nature of your data and the type of processing needed to perform.