Databricks Certified Data Engineer - Professional

Explanation

In Apache Spark Structured Streaming, the .load() method is used to create a streaming DataFrame from a data source. Here's why:

• .load() - This method triggers the actual loading of data from the specified source (Kafka in this case) and returns a streaming DataFrame that can be used for further transformations and operations.

• .print() - This is used for debugging purposes to print the schema of the DataFrame, not for loading data.

• .return() - This is not a valid method in Spark DataFrame API for loading data sources.

• .merge() - This is used for merging DataFrames in batch processing, not for loading streaming data sources.

The correct sequence for creating a streaming DataFrame from Kafka is:

1. spark.readStream - Create a streaming reader
2. .format("kafka") - Specify the data source format
3. .option() - Set various configuration options
4. .load() - Load the data and return a streaming DataFrame

This streaming DataFrame can then be used with operations like .writeStream to output the processed data to various sinks.