The query performing a streaming hop from raw data to a Bronze table is identified by using the

Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:

Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of

a roll-up into a summarized table rather than a hop into a Bronze table.

Option B: Also uses .writeStream but calculates an average, which again does not typically represent

the raw to Bronze transformation, which usually involves minimal transformations.

Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and

hence not suitable for real-time streaming data transformation to a Bronze table.

Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out

with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is

captured in real-time and minimal transformation is applied. This approach aligns with the concept

of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored

in a more accessible format.

Reference:

Databricks documentation on Structured Streaming: Structured Streaming in Databricks