What format should you use to write a Spark dataframe to a Delta Lake table?

Using the Delta format to write a Spark dataframe to a Delta Lake table is the most appropriate choice because Delta Lake is built on top of Apache Parquet and extends it to provide features such as ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. When writing a Spark dataframe to a Delta Lake table, specifying the format as DELTA allows Spark to leverage these features, ensuring data is stored efficiently and is fully transactional.

Choosing a different format like CSV, Parquet, or JSON would not utilize the capabilities of Delta Lake, such as schema evolution or handling of concurrent writes, which are essential for maintaining data integrity and consistency in modern data applications. These alternative formats, while useful for other scenarios, do not offer the same robust functionality as the Delta format when interacting with Delta Lake tables.