In Spark Structured Streaming, a Delta Lake table can function as which of the following?

In Spark Structured Streaming, a Delta Lake table can function as either a source or a sink, making it a versatile option for managing streaming data workflows. As a source, a Delta Lake table can be read to ingest data for processing. This allows users to continuously pull the latest changes from the Delta Lake, which is essential in scenarios where data is constantly being updated or appended.

On the other hand, a Delta Lake table serves as a sink when processed data is written to it. This capability is crucial for saving the results of stream processing or aggregation operations back into a Delta Lake table, ensuring that data can be queried later or used in other applications.

This duality enables developers to build robust streaming applications that require both continuous input and durable output, enhancing the efficiency and flexibility of data workflows. The ability to utilize Delta Lake in both roles underscores its effectiveness in handling modern data scenarios in Spark Structured Streaming.