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

In Spark Structured Streaming, a Delta Lake table can indeed function as either a source or a sink, making this option the correct choice. As a source, Delta Lake allows Spark to read streaming data that is ingested into a Delta table, enabling real-time analytics and processing directly off the data stored in those tables. This is particularly useful for applications requiring continuous access to the most recent data.

On the other hand, as a sink, Delta Lake can store the results of streaming computations. As your streaming jobs process incoming data, they can write processed data or aggregations back to a Delta table, allowing for easy integration with subsequent processing or analysis tasks.

This dual functionality is a key benefit of using Delta Lake within Spark Structured Streaming, as it supports a seamless flow of data in both directions—reading from and writing to Delta tables—thereby enhancing the capabilities of real-time data engineering tasks and workflows. This flexibility is critical for building complex data pipelines that require ongoing data ingestion and transformation.